Thehttp://nro.sagepub.com/ Neuroscientist Brain−Computer Interface after Nervous System Injury Alexis Burns, Hojjat Adeli and John A. Buford Neuroscientist published online 5 September 2014 DOI: 10.1177/1073858414549015

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NROXXX10.1177/1073858414549015The NeuroscientistBurns and others

Review

Brain–Computer Interface after Nervous System Injury

The Neuroscientist 1­–13 © The Author(s) 2014 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1073858414549015 nro.sagepub.com

Alexis Burns1, Hojjat Adeli2, and John A. Buford3

Abstract Brain–computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a stateof-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson’s disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders. Keywords computational neuroscience, signal processing, wavelets, Parkinson’s disease, brain-computer-interface

Introduction The treatment of nervous system injury is extremely delicate because of the distributive organization of the central nervous system (CNS). Because of this nature, damage to the CNS causes widespread detrimental effects to the sensorimotor system, including muscles commanded by damaged cells. While diagnosis and rehabilitation of patients with nervous system injury are well researched, few of these disorders possess a cure. In years to come, effective and minimally invasive treatments for nervous system injury will be achieved as related research evolves; however, current patients suffering from nervous system injury need reprieve amidst gradually improving research. Focus on improvement of rehabilitation and quality of life for damage caused by nervous system injury is imperative for people suffering now. Current methods of diagnosis, rehabilitation, or quality-of-life improvement for nervous system injuries such as amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), spinal cord injury (SCI), stroke, and disorders of consciousness (DOC) can be assisted or improved by brain–computer interface (BCI) technology (Y. Zhang and others 2014). BCI research has been successful in providing patient-controlled compensation for the loss of muscle movement and communication through devices such as robotic arms and spelling programs. BCI systems are designed to acquire electrical signals from the brain and process them into commands for effector devices to perform the patient’s desired action. The functions of a BCI can be broken down into three categories: signal

acquisition, signal processing, and the function of the effector device. There are a variety of developed methods for each component and the heterogeneous combination of the three allows for customization of the BCI to specific needs of the disease. The purpose of this article is to present a state-of-theart review of BCI used by patients with the aforementioned nervous system injuries. The main goal of BCI is to improve the quality of life for these patients through rehabilitation and control of replacement devices. The focus of the review is on human studies, but significant studies using monkeys are also included and noted as such. Excitingly, BCI research is evolving to allow patient brain control of treatment. In other words, patients can control their own treatment with BCI.

1

Biomedical Engineering Graduate Program, The Ohio State University, Columbus, OH, USA 2 Departments of Biomedical Engineering, Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science, Electrical and Computer Engineering, and Neuroscience, and the Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA 3 Physical Therapy Division, School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, OH, USA Corresponding Author: Hojjat Adeli, Departments of Biomedical Engineering, Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science, Electrical and Computer Engineering, and Neuroscience, and the Biophysics Graduate Program, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH 43210, USA. Email: [email protected]

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Table 1.  Modes of Signal Acquisition for Brain–Computer Interface. Type fMRI

EEG

ECoG

LFP

Advantages

Disadvantages

Two magnetic fields + energy-detecting coil •• Can portray where the activity is •• Expensive occurring in the brain •• Large size •• Patient must be in supine position and still Scalp electrodes •• Non-invasive •• Low spatial resolution •• Multidimensional BCI responses •• Filters necessary to eliminate artifacts from •• A variety of signals can be detected nearby muscles (P300, VEP, SCP, SMR, etc.)   Cortical surface electrodes •• Higher spatial resolution than EEG •• Invasive (limits research as well as desirability) •• Higher SNR than EEG •• Must be biocompatible •• Wider frequency range than EEG •• Risk of tissue rejection/infection Cortically and deeply penetrating electrodes •• Highest spatial resolution and SNR •• Invasive •• Must be biocompatible •• Risk of tissue rejection and glial scarring

BCI = brain–computer interface; ECoG = electrocorticography; EEG = electroencephalography; fMRI = functional magnetic resonance imaging; LFP = local field potential; SCP = slow cortical potential; SMR = sensorimotor rhythm; SNR, signal to noise ratio; VEP, visually evoked potential.

Signal Acquisition There are invasive and non-invasive techniques for signal acquisition of brain signals (Table 1). Non-invasive techniques do not require surgical implantation and acquire signals without penetrating the skin. It is the preferred method, as patients wish to avoid the risks of major surgery. In contrast, invasive techniques acquire brain signals by penetrating the skin and removing a piece of the skull to place electrodes on, in, or underneath the cortex. Their neuronal proximity provides more information about the brain signal with less noise; however, the invasiveness is less desirable for clinical applications. Most invasively acquired human brain signals for BCI research come from patients who already possess implanted intracortical electrodes for deep brain stimulation treatment of epilepsy (Vonck and others 2013) or PD (Mukamel and Fried 2012). Biocompatible microarrays consisting of single electrodes are arranged in a matrix that either sit on top of the cortex or penetrate its surface (Thakor 2013). There are three popular types of electrodes for invasive recording (Figure 1), each functioning to detect a separate type of signal. (1) Cortical surface microarrays are used to obtain electrocorticographic (ECoG) signals from the surface of the cortex only. (2) Cortical penetrating microarrays collect single-unit action potentials and local field potentials (LFP), which are cellular signals collected from the extracellular matrix of neurons and their axons. (3) Deeply penetrating electrodes target neurons of subcortical structures and are used to detect LFP and single-action potentials as well (Mukamel and Fried 2012).

Electroencephalography (EEG) is the most popular non-invasive technique used for BCI research involving the treatment of neurological diseases. With EEG electrodes being positioned on the scalp, other signals such as electromyographic (EMG) signals from the face, eyes, and so on, must be filtered out of the signal for effective use in BCI technology. It is also necessary for the EEG signal-to-noise ratio to be increased via signal processing due to the distal position of the electrodes in relation to the cortex. Signal processing techniques remove noise and can detect features in the EEG signals for BCI use.

Signal Processing Brain–computer interfaces for neurological diseases typically use EEG signals such as sensorimotor rhythms (SMRs), slow cortical potentials (SCPs), event-related potentials (ERPs), and visually evoked potentials (VEPs) (Figure 2). SMRs are produced by the sensory and/or motor cortical areas and contain the subcategory referred to as motor imagery (MI) signals (Rodríguez-Bermúdez and others 2013). MI signals are generated by the imagination of motor movements that does not require the actual movement of muscles. SCPs are slow voltage changes that occur in the cortex and are recorded on the scalp using an EEG. They can be negative or positive signifying whether there is increased or decreased cortical activation, respectively. ERPs require an external stimulus to evoke and record an electrical brain response. P300 signals are ERPs where a positive peak occurs 300 to 400 ms after the external stimulus. A VEP is a more specific

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Figure 1.  Types of brain signal acquisition. Electroencephalographic (EEG) electrodes located on top of the scalp are able to access the signals from neurons indirectly. Cortical surface microelectrodes sit on top of the cortex and have direct access to electrocorticographic (ECoG) signals from numerous surface neurons. Local field potentials are acquired from fewer neurons than EEG and ECoG through cortical penetrating microelectrodes and provide a cleaner less noisy signal. Single-unit action potentials can be acquired through deep brain electrodes or cortical penetrating microelectrodes. These signals are generated from contact with only one neuron.

ERP using visual stimuli only. Steady state VEPs (SSVEP) (Y.U. Zhang and others 2013) consist of flashing lights of different frequencies to evoke a constant frequency brain response. All of these signals are detected using signal processing techniques that alter the filtered EEG signal. The frequency of the EEG signal determines its signal category (Adeli and Ghosh-Dastidar 2010; Rangaprakash and others 2013; Serletis and others 2013). Regarding EEG and ECoG recordings, specific frequency bands, such as theta, gamma, beta, and so on, have been characterized by their cortical location, bodily function, and pathological association. After filtering out EMG artifacts, these frequencies can be used to analyze the waveform for detection and characterization of patient intentions to be relayed to the effector device. Identification of these frequency bands requires a signal transformation because the initial brain signal is acquired in the time domain, that is, the voltage amplitude of the signal is plotted over time. For years, the Fourier transform was used for transforming the time series signal to the frequency domain, i.e. voltage amplitude plotted against frequency bands. When

most of the signal resides in a particular frequency band, a peak with large amplitude will reside in that portion of the graph and can be detected via classification methods. More current signal processing techniques (Table 2) used for BCI devices are the common spatial filter (CSF), Kalman filter, and wavelet transform (WT) (Hsu 2013; Perez and others 2014). CSF analyzes the signal in its spatial arrangement and therefore it is necessary to use a microarray. The interactions of each electrode are analyzed with respect to its neighboring electrodes to identify differences in variance to determine noise signals and signal features. WT provides a multi-resolution time– frequency analysis where a signal is decomposed into components in the frequency and time domains simultaneously and has multiple layers containing information from different levels of resolution (Figure 3). The most used classification techniques presented in this paper include linear discriminant analysis (LDA), radial basis functions (RBFs) (Alexandridis 2013; Siddique and Adeli 2013; Zhou and others 2013), and support vector machines (SVMs) (D. Li and others 2013). Recently, OrtizRosario and Adeli (2013) presented a review of BCI

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Figure 2.  Electroencephalographic (EEG) signal categories. Sensorimotor rhythm (SMR) signals are recorded as voltage over time and analyzed in the frequency domain to determine the strength of the various frequencies within the recorded signal. The peaks in the lower SMR graph show the strength of the corresponding frequencies identified in the upper SMR graph. Eventrelated potentials (ERPs) require an external stimulus for occurrence and are typically analyzed in the time domain. The N400 is typically stimulated with auditory stimuli while P300 and steady state visually evoked potentials (SSVEPs) are visually stimulated. Characteristic of an SSVEP feature is a constant frequency different from the rest of the signal. Slow cortical potentials (SCPs) are also analyzed in the time domain and are characterized by gradual signal increases or decreases over time.

technologies with more focus on signal processing approaches. Through a combination of filtering, transformation, and classification algorithms, acquired signals can be processed into a digital command.

instruments, automated treatment, wheelchairs, prosthetics, virtual reality programs and externally operated robotic limbs is presented for each disorder along with signal acquisition and processing techniques.

Effector Devices

Brain–Computer Interface for Neurological Rehabilitation

The effector device receives digital commands processed from the analog brain signal. Specific development of devices for BCI is not altogether necessary, for the effector device can be anything that can be programmed to receive commands for its designed function. The main effector devices discussed in this review assist in the diagnosis, rehabilitation, or quality-of-life improvement of nervous system injury and therefore provide communication or motor repair/replacement. In the following section research regarding spelling apparatuses, orthotic

Amyotrophic Lateral Sclerosis Research on Healthy Subjects. P300 and SSVEP are the most commonly used signals in BCI spellers. These spellers allow patients to choose characters from a virtual keyboard and communicate via typed messages. Research regarding BCI spellers for ALS patients focuses on improving speed and accuracy to enhance ease of communication and make the use of these devices more natural.

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Burns and others Table 2.  Main Signal Processing and Classification Techniques Used in Brain–Computer Interface. Common spatial filter

Advantages: CSF is useful in obtaining brain signals through the establishment of spatial filters designating where these signals occur.   Disadvantages: CSF necessitates large training samples of EEG data, identical placement of electrodes for each trial, and is limited to classifying linear clusters. Kalman filter Advantages: Adaptive learning method. It increases SNR while including more information about the signal   Disadvantages: The trade-off for a higher SNR is a slower processing speed. It is limited to linearity but can be improved with the extended Kalman filter. Wavelet transform Advantages: Capable of analysis in time and frequency domains simultaneously, local analysis of smaller sections within a larger signal, and varying bandwidths of wavelets.   Disadvantages: Some wavelet transforms can be redundant or computationally expensive, which can be regulated by altering parameters of that specific transform. Linear discriminant Advantages: LDA detects error at a analysis faster rate than SVM.   Disadvantages: The faster detection rate exists with the sacrifice of accuracy in error detection for LDA, which is also limited to linear clustering. Support vector Advantages: More accurate error machine detection than LDA; Prevents data overfitting.   Disadvantages: The trade-off for higher accuracy is a slower error detection rate due to the use of optimization parameters. Radial basis Advantages: RBF converges fast during function training and has a good true positive rate.   Disadvantages: While RBF trains quickly, they are slower in online use. Offline versus Online Training for BCI use typically consists of offline data analysis. Offline: It does not include the feedback given to the user from the BCI. It determines algorithm accuracy. Online: This represents the fully-functioning BCI system, where the effector device is receiving the commands based on the brain signal classification identified during signal processing stage. BCI = brain–computer interface; CSF = common spatial filter; LDA = linear discriminant analysis; RBF = radial basis function; SNR = signal to noise ratio; SVM = support vector machine.

Figure 3.  Wavelet transform signal processing. The fourstage diagram roughly describes the basic process of the wavelet transform. In stage 1, the original signal is acquired from the patient. The signal is broken down into multiple resolutions containing details of the signal in stage 2. The form presented here is specific to the discrete wavelet transform. Stage 3 shows the intensity of frequency bands in the signal over time, which is then filtered to remove noise. Stage 4 shows the resulting denoised signal.

Within the realm of P300 spellers, several researchers focus on improvement of signal acquisition by altering the character selection mechanism. The row–column paradigm (RCP) is the first of many various keyboard schemes used for character selection in BCI spellers. The patient first selects the column in which the desired character resides and follows with a selection of the row

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containing the letter. Another keyboard scheme, the checker-board paradigm (CBP), addresses the issues with distraction and double flashing caused by adjacent characters being intensified simultaneously with RCP. The CBP has a higher reported accuracy (92%) and speed (23 bits/min) compared with that reported for the RCP (77%; 17 bits/min) (Townsend and others 2010). Postelnicu and Talaba (2013) alter the CBP by dividing it in half and coupling electrooculography with EEG for the patient’s matrix half selection. The authors achieve an increase in speed, however, complete locked-in syndrome (CLIS) patients are not able to control their ocular muscles to blink and their eyes are typically closed. In order to communicate letter positions to mute, blind, or deaf ALS patients, Kathner and others (2013) describe an EEGbased BCI system that enables communication via P300 with both RCP and auditory tones using 20 healthy participants. To increase the speed and accuracy of SSVEP-BCI spellers toward a natural writing style, İşcan and Dokur (2014) introduce a visual stimulation design that allows the user to draw characters in a grid. Sixteen healthy subjects perform within a range of 90% to 100% accuracy and an average speed of 35 bits/min (İşcan and Dokur 2014). With a similar goal in mind, Akram and others (2014) use a combination of RCP, a word-selection program, and SVM to increase the speed of letter selection to 31 seconds and word selection to about 2 minutes in 10 healthy subjects. SSVEP-BCIs usually associate each target with one frequency, leading to a limited amount of targets to be linked to frequencies and used as commands. To increase the number of targets, Chen and others (2013) designate two frequencies per target in an SSVEP-BCI and vary the frequencies of two sensory stimuli, luminance and color, to classify user target selection. Manipulating these frequencies, 14 healthy subjects and one chronic stroke patient are able to select the targets at a speed of 34 bits/ min and with a 93% accuracy (Chen and others 2013). Some researchers combine the acquisition of more than one signal to enhance the usability of BCI spellers. Yin and others (2014) report a speed of 53 bits/min in a study where 14 healthy participants simultaneously evoke P300 and SSVEP potentials for letter selection with the RCP. Xu and others (2013) use a similar combination but instead block the SSVEP potential, increasing the accuracy to 93% from 91% of a P300-only speller. Research on ALS Patients.  Because of the inability of motor function for locked-in syndrome (LIS) patients, including all facial muscles for CLIS patients, gaze-control limitations must also be considered. Marchetti and others’ (2013) BCI extracts ERPs stimulated by either exogenous or endogenous visuospatial attention orienting, which rely

on peripheral or central visual attention, respectively, not eye movement. This object-selection BCI uses an SVM classifier to select the signals and allows ten ALS patients to use the interface requiring endogenous visuospatial attention orienting with 70% accuracy (Marchetti and others 2013). Attempting to increase P300-BCI speller reliability, De Massari and others (2013) introduce a BCI that uses semantic conditioning to induce a conditioned response from ALS-LIS or ALS-CLIS patients. The conditioned response is classified as intent using discrete WT and a RBF kernel followed by an SVM (D. Li and others 2013). While successful communication is achieved by one ALS-LIS patient in this study, the two older ALS patients in this study were not successful, possibly because of decrease in cognitive alertness developed postinjury. Taking ALS patients’ quality of life a step further, Münßinger and others (2010) along with Zickler and others (2013) develop a P300-based BCI allowing patients to paint free form drawings with a stepwise LDA (SWLDA) for signal classification. The interface tasks consist of (a) redrawing a given painting and (b) a free-form drawing, where the patient paints without a template (Zickler and others 2013). In the study by Münßinger and others (2010), three ALS patients demonstrate the ability to control the painting application and two of them achieved a 90% accuracy and 5 bits/min information transfer rate (ITR). In Zickler and others, the focus is more on freeform painting feasibility consisting of 5 free-form painting trials for each participant. They report accuracy in the range 86-93% for four ALS patients and an ITR range of 4 to 5 bits/min in the last free-painting trial. In both studies, the ALS patients report feeling very satisfied, interested, challenged, and motivated.

Parkinson’s Disease Direct access of BCI to damaged neurons can be applied to diagnostic practices (C. Liu and others 2013). Santaniello and others (2012) improve the real-time transition state detection of movement-related events by combining a hidden Markov model with Bayesian estimation to detect error from previous bioelectrical events. With electrodes in the subthalamic nucleus (STN), seven participants with PD manipulated a joystick using a computer program that correlated the patient’s neuronal output with their actions. This method increases the speed of detecting onsets of physiological events by adjusting the detection threshold based on a posteriori probability. It is intended to be implemented in a device to assist with the understanding and diagnosis of PD (Santaniello and others 2012). Mace and others (2013) tailor ERP extraction to PD patients by adapting the threshold to detect the ERP

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Burns and others resulting from movement without false signal detection due to noise from other brain signals occurring simultaneously. Their algorithm adapts the threshold according to a short-term energy-dependent detection contour along which the LFP value is determined. The threshold adaptation and signal detection are then encoded into a binary output that can be used for movement detection in neurosurgery as well as a BCI-driven diagnostic tool for PD (Mace and others 2013).

Spinal Cord Injury Research on Healthy Subjects.  A BCI can distinguish neurons discharging during actual movement and those discharging during MI. As such, BCI for limb prosthetics provides a direct path of motor control for paralyzed individuals (Ganguly and others 2011). When a patient is awake but in a resting position, EEG signals are still sent to the BCI but are processed to inhibit prosthetic movement (Velliste and others 2008). To enhance the natural feeling of these neural prosthetics, research continues to increase the accuracy and speed of command translation. Gilja and others (2012) use a Kalman filter adaptation in order to handle not only the position but also the velocity of a cursor in relation to neural activity, instead of keeping the velocity constant, which is commonly done in neural prosthetics. They test this concept with a BCI that controls a cursor on two monkeys for 4 years and showed their adaptation achieves 75% of the speed and 72% of the accuracy of the native arm (Gilja and others 2012). Rouse and others (2013) conducted a similar study using alternate signal acquisition and processing techniques. In another attempt to make neural prosthetic movement more natural, Shanechi and others (2012) present a BCI capable of performing two sequential tasks involving moving a cursor to two separate targets, simulating complex movements typical of native arm movement. To predict the two sequential tasks the BCI estimates the action potential firing rate of each recorded premotor cortex neuron using an inhomogeneous Poisson process followed by an expectation-maximization iterative algorithm. Two monkeys perform complex movements at an accuracy of 75% with this BCI. More than one cortical area is utilized for various natural movements, necessitating complex neuroprosthetic systems. BCIs define the degrees of freedom (DOF) in a system as a representation of various directions and types of movement; the human hand has 22 DOF. McMullen et al. address the large gap between DOF of BCIs and robotic technology with the largest numbers reported so far to be 7 and 17 for the two, respectively. This gap keeps the patient from using the robotic arm to its fullest potential for creating a more natural control. By integrating eye-tracking technology with MI, two subjects select an

object and initiate movement in a robotic arm with an object-selection accuracy ranging from 68% to 71% (McMullen and others 2014). Orsborn and others (2014) use different signal processing techniques depending on the variation of neural signals acquired from two monkeys performing various movements. They show an increase in neuroprosthetic skill. The BCI system consists of multiple pathways for movements, making it more viable in everyday applications (Orsborn and others 2014). Ethier and others (2012) show MI-triggered FES through a BCI can initiate movement in the affected limb by a monkey induced with a spinal cord lesion. The MI signals are recorded from the motor cortex and converted into FES signals sent to surgically implanted microwires in the arm and hand. The monkey is able to control grasping movement, a function which was not possible after the SCI. A similar study was conducted by Nishimura and others (2013) using LFP to induce FES of wrist movement. SCI patients need to be able not only to reach and grasp items in their daily lives but also require the ability to move around their household. J. Li and others (2013) present a wheelchair BCI allowing 12 healthy participants to choose their destination, eliminating the need for preset destinations and report 82% accuracy. Another group, J. Li and others (2014), test a hybrid MI and SSVEP-BCI on seven healthy patients that has five DOF allowing for various levels of speed control. Wheelchair mobility is only the tip of the iceberg; feasible prosthetic lower-limb control is shown in virtual reality studies requiring subjects to use MI to control a virtual leg (Pfurtscheller and others 2010). Research on SCI Patients. A non-invasive version of the FES-BCI system mentioned in the previous section is applied in a clinical setting. Rohm and others (2013) combine SMRs derived from the EEG with an FES-BCI designed to induce movement in the hand, fingers, and elbow of an SCI patient. The patient attains an average of 70% accuracy with this BCI using an LDA classifier (Rohm and others 2013). Wheelchair and lower-limb prosthetic movement have also been implemented in clinical trials. Y. Li and others (2013) develop a hybrid-BCI capable of multidimensional control, specifically, the 2D control of a wheelchair. MI and P300 are coupled to control speed and direction while SSVEP and P300 are coupled to improve the asynchronous BCI’s ability to distinguish between go and stop commands using an SVM classifier (Y. Li and others 2013). From a prosthetic approach Do and others (2013) show the ability of an SCI patient to control a robotic-leg orthosis with MI, achieving an offline accuracy of 86%, comparable to an average 90% accuracy of myoelectric prostheses for SCI amputees (Hargrove and

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others 2013). These studies are an exciting development toward the free movement of SCI patients. Typing is an essential form of communication in modern society that is claimed by SCI. Perdikis and others (2014) present an MI-BCI speller used by five tetraplegic patients and three patients with other disorders causing paralysis. The power of the MI signals is classified using a Gaussian mixture model. They report an average 94% accuracy and a maximum speed of 6.6 characters per minute (Perdikis and others 2014).

Stroke Research on Healthy Subjects.  Stroke patients with minimal loss of motor control have had success with the use of virtual reality systems in physical therapy. Applying this to BCI, Bermúdez i Badia and others (2013) pair MI and SMR with a virtual reality (VR) stroke rehabilitation program to provide better feedback to patients with damaged motor control. Nine healthy subjects observed, imagined, and performed motor activity in different trials where a moving average linear classifier is used to detect and transform SMR and MI signals to commands for an arm in virtual space. The authors report a functional success rate of 85% (Bermúdez i Badia and others 2013). Research on Chronic Stroke Patients.  In order to improve the quality of life for paralyzed stroke survivors, Hochberg and others (2012) demonstrate the ability of two longterm, tetraplegic stroke patients to control the reaching and grasping movements of a robotic hand with MI signals. Calibration for reaching is done with a Kalman filter to estimate intended velocity of the robotic arm, and LDA is used for classification of the robotic hand’s grasping motion. For the reaching movement, the two subjects show a success rate of 69% and 95%. For the grasping movement, they show a success rate of 46% and 62% (Hochberg and others 2012). Yanagisawa and others (2012) demonstrate ECoG control of a prosthetic arm by paralyzed patients using the high range of frequency of the motor cortex signals. Seven patients suffering from varying degrees of motor dysfunction caused by stroke or SCI first train by attempting specific arm movements and then can control the arm by performing the same movements. The authors use SVM as the classifier and Gaussian process regression to detect and extract the ECoG signals for command use without over- or underfitting the data collected. One of the seven patients was successful in online prosthetic arm control (Yanagisawa and others 2012).

Disorders of Consciousness Research on Healthy Patients.  The inability to use a P300 speller may make a minimally conscious patient seem like

they are not aware, but there are other ways to use BCI devices to detect awareness. As a proof-of-concept LopezGordo and Pelayo (2013) use an auditory, ERP-based BCI on twelve healthy participants to detect a person’s attention to human speech. Two separate, discrete sounds are played in either ear of the subject who generates an ERP by focusing on one of the sounds. The ERP is detected by the BCI using binary phase-shift-keying to detect features from the two counter-phased signals (Lopez-Gordo and Pelayo 2013). Müller-Putz and others (2013) use beta oscillations following MI signals of one hand and one foot to decipher between yes and no answers from patients. Three healthy subjects are able to communicate with 90% accuracy. Communication studies using fMRI have also been conducted on monkeys and healthy patients involving selective attention on provided answers and visual tasks (Naci and others 2012; Rotermund and others 2013). Research on Minimally Conscious Patients. Awareness in minimally conscious patients, including coma, LIS/CLIS, and vegetative state patients, is difficult to detect, but if detected will allow patients’ family members to make an informed medical decision. The ability of these patients to communicate by executing a task, such as deliberate eye-blinking or any other purposeful movement, is more difficult to detect because of disability (Fernández-Espejo and Owen 2013). Monti et al. detect awareness in 5 out of 54 DOC patients by quantitatively analyzing the regions of interest from fMRI scans using an average generalized linear model estimate (Monti and others 2010). Lulé and others (2013) use an auditory P300 BCI speller with an SWLDA classifier on 18 coma survivors where one patient successfully communicates with the device. As research progresses more accurate awareness detection is proving to be possible. Pokorny and others (2013) detected awareness in 9 out of 12 DOC patients using a P300 BCI with SWLDA for classification.

The Future of Brain–Computer Interface Research Brain–Computer–Brain Interfaces Up until recent years, BCI research has focused on replacing lost motor functions using devices. For example, the loss of speech is replaced with a brain-controlled keyboard; the loss of arm movement is replaced with a braincontrolled robotic arm. Embarking on a new path, BCI research now involves affecting brain plasticity with the intention to treat neurological disorders with cortical reorganization. The term brain–computer–brain interface (Figure 4) refers to a BCI that uses either direct stimulation to the body or an effector device to alter and strengthen neuronal firing.

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Figure 4.  Brain–computer–brain interfaces (BCBIs). Brain–computer–brain interfaces differ from typical BCIs because of their direct impact on the human body. Illustrated in this figure are methods that effect neuronal firing and muscle stimulation in order to induce cortical reorganization via plastic changes in the brain.

Because several neurological disorders have been linked to asynchronous gamma frequency (Buzsáki and Wang 2012), Engelhard and others (2013) use operant conditioning through BCI to increase two monkeys’ control over LFP gamma oscillations in the motor cortex and consequently increase neuronal synchrony. This method’s potential for reversing the asynchrony in neurological disorders, such as Alzheimer’s disease (Adeli and others 2005a; Adeli and others 2005b; Adeli and others 2008; Ahmadlou and others 2010; Ahmadlou and others 2011), autism (Ahmadlou and others 2012), and attention deficit/hyperactivity disorder (Ahmadlou and Adeli 2010), is promising for clinical applications. Adding a neurofeedback parameter to BCI has proven to be helpful in increasing volitional control of brain signals (Lawrence and others 2013) and is being researched for patient-customizing of BCIs. O’Doherty and others (2011) provide a proof-of-concept regarding the ability for a user to achieve skillful motor control using a BCI coupled with tactile sensory feedback. The authors trained two monkeys to control a virtual reality arm by providing stimulation pulses to the primary motor area of the brain with different frequencies and time intervals to represent correct and incorrect actions. One monkey

demonstrates an accuracy of 73% in brain control while moving its hand, while the other monkey performs with 50% accuracy (O’Doherty and others 2011). Using neurofeedback in BCI technology to affect brain plasticity, Florin and others (2013) demonstrate the adaptation in a healthy user’s brain by using magnetoencephalography and providing the patient with a virtual representation of the plastic changes occurring throughout the user’s training. With this information the patient is able to adapt his/her strategy for BCI control (Florin and others 2013). Also exploring this concept, Hampson and others (2013) propose a multi-input multi-output model to mimic spatial and temporal stimulation patterns in the hippocampus of rhesus macaque monkeys. It uses a set of first-order, non-linear differential equations to model the postsynaptic potentials followed by a feedforward kernel neural network to convert the input into signal output commands. The goal is to strengthen the synapses required for specific memories to each individual with a neural prosthetic in patients with hippocampal memory impairment, such as Alzheimer’s disease. During memory encoding tasks multi-neuron recordings are used to acquire the task-related stimulation patterns. To induce the same memory encoding brain function, these patterns are fed into the model and output as stimuli to the hippocampus (Hampson and others 2013). Some stroke survivors experience slight recovery due to naturally occurring plastic changes following injury, but increased recovery can be obtained by inducing plastic changes (Bradnam and others 2012). MrachaczKersting and others (2014) describe a BCI that induces cortical plasticity as a rehabilitation method for thirteen chronic stroke patients. They acquire negative SCPs: known to be movement-related and are generated from imagining a specific motor movement. In this study the signal is band-pass filtered, wavelet-denoised and windowed to identify the peak negativity, which determines the times a stimulus is sent to an afferent nerve for brain signaling. Movement-related cortical potentials recorded from the patients increase in amplitude, portraying strengthening affects to damaged brain areas (MrachaczKersting and others 2014). Normal beta frequency oscillations (13-30 Hz) that occur in the STN are disrupted by bursts in activity in PD patients. Deep brain stimulation (DBS) is a therapeutic technique designed to reduce the occurrences of beta bursts, however, it only allows for continuous stimulation with an open-loop system that does not include neural feedback (Kringelback and others 2007). Little et al. present proof-of-principle for BCI-controlled adaptive DBS by recording beta frequencies in LFPs from the STN. Feedback is provided by processing the beta activity to determine the stimulation threshold for 8 patients with advanced PD. The goal is to personalize and

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optimize DBS stimulation. By altering the threshold the BCI is reportedly able to decrease the three main signs of PD—tremor, bradykinesia, and rigidity—progressively as well as reduce the total battery energy consumed by the device compared to conventional DBS (Little and others 2013). Physical therapy techniques for stroke rehabilitation rely heavily on neuronal plasticity and use robotics to reduce the need for adept neuronal–muscular connections while increasing the strength of alternate connections (Lo and others 2010). Ramos-Murguialday and others (2013) use an SMR-driven BCI that controls an upper limb orthosis in 32 chronic stroke patients. The authors conduct a double-blind study requiring all participants to desynchronize the SMR signals in the motor cortex ipsilateral to the lesion when attempting to move the paretic limb. Accurate feedback of orthotic movement in the experimental group results in increased amplitude and frequency of the paretic arm’s EMG activity as well as improved Fugl-Meyer assessment scores, typically used in physiotherapy. A majority of the experimental subjects also showed a change in activity in the motor and premotor cortex from the contralateral to the ipsilateral side of the lesion, indicating occurrence of plastic changes to better control the paretic arm. This proof-of-concept study indicates a link between BCI training and neural plasticity that can increase the effectiveness of stroke rehabilitation (Ramos-Murguialday and others 2013). Similarly intending to induce cortical reorganization, Y. Liu and others (2014) present an algorithm for a FESBCI system using a tensor-based feature line reflecting the combinatorial effect of differences in space, time, and frequency to more accurately identify MI patterns that differ significantly in stroke patients from healthy patients. The signal is processed using WT and classified by Euclidean distances, least squares, and gradient descent methods for the prediction and identification of the most discriminative MI patterns. SVM is used for online classification where eight stroke subjects imagine reaching and grabbing a drinking glass and are visually reinforced with virtual reality arm movement as well as physically reinforced with FES to the muscles involved. Significant motor improvement is achieved by the stroke subjects through training with the FES-BCI (Y. Liu and others 2014). It is more difficult to achieve cortical reorganization in SCI patients than in stroke patients due to the physically disrupted connection between muscle and cortex. Lucas and Fetz (2013) present induced cortical reorganization via conditioning with consecutive stimulation of the cortex and arm muscles. Neurochips implanted into four monkeys’ M1 cortical areas are programmed to deliver cortical stimulation in concordance with muscle microstimulation. External muscle stimulation is removed from

the system and is replaced by natural muscle activities resulting from conditioned cortical stimulation (Lucas and Fetz 2013).

Conclusions An important goal for BCIs is to improve the quality of life for incurable neurological disorders. BCI for the treatment of neurological disorders is constantly improving thanks to research focused on replacing motor functions with computerized devices or restoring motor function by affecting neural plasticity. In the coming years BCIs for motor function will become increasingly natural by increasing device control speed and the degrees of freedom of signal acquisition to match that of the effector devices and natural limbs. The most exciting BCI technology is evolving to provide therapeutic results by inducing plastic changes in the brain using neurofeedback such as DBS, FES, and cortical stimulation. With this step forward, BCI will eventually be implemented into rehabilitation practices for more interactive and speedy recovery from symptoms of nervous system injury. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Brain-computer interface after nervous system injury.

Brain-computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous ...
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