Neural Rehabilitation Research Articles

Interface, interaction, and intelligence in generalized brain–computer interfaces

A brain-computer interface (BCI) establishes a direct communication channel between a brain and an external device. With recent advances in neurotechnology and artificial intelligence (AI), the brain signals in BCI communication have been advanced from sensation and perception to higher-level cognition activities. While the field of BCI has grown rapidly in the past decades, the core technologies and innovative ideas behind seemingly unrelated BCI systems have never been summarized from an evolutionary point of view. Here, we review various BCI paradigms and present an evolutionary model of generalized BCI technology which comprises three stages: interface, interaction, and intelligence (I3). We also highlight challenges, opportunities, and future perspectives in the development of new BCI technology.

A simulation-based framework with a proprioceptive musculoskeletal model for evaluating the rehabilitation exoskeleton system

Background and objectiveVarious rehabilitation exoskeletons have been designed to help people regain normal gait from stroke effects. However, the evaluation and further optimization of these exoskeletons are not convenient and usually need complicated experimental works. The present study aims to establish a simulation-based method with a proprioceptive musculoskeletal model to conveniently evaluate the efficiency of a self-developed exoskeleton for further optimization. MethodsThree volunteers who suffer from dyskinesia due to stroke were recruited for gait experiments with and without the self-develop exoskeleton. The corresponding simulations were implemented based on the proprioceptive model, the exoskeleton model, and the input kinematic data obtained from the experiments. The joint angles, muscle activations, and metabolic costs as well as the proprioceptor feedback stimulation were extracted for comparative analysis. ResultSeveral positive effects of the exoskeleton were noted based on the simulation results when using it to aid the patients’ rehabilitation during the gait training. The CORA scores of the patients’ joint angle to the normal data increased by 11.6~37.8% with the assistance of the exoskeleton. The wave frequency of proprioceptive feedback stimulation that can be directly correlated to the neural rehabilitation obviously inclined during a gait cycle. The muscle activations were also rearranged to better support the patient's walk when using the exoskeleton, while the metabolic costs were reduced for all the patients. ConclusionIn summary, the present simulation-based method can be practical for pre-evaluation and optimization of various exoskeleton design in the future.

EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application.

Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain-computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on μ-ERD (ΔLCm-ERD) had high significant positive correlation with the change in FMA-UE(r = 0.6093, P = 0.012), which provides theoretical basis for exploring novel objective evaluation methods.

Converging Robotic Technologies in Targeted Neural Rehabilitation: A Review of Emerging Solutions and Challenges.

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals—namely, family members and professional carers—to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings.

We provide an open access dataset of High densitY Surface Electromyogram (HD-sEMG) Recordings (named "Hyser"), a toolbox for neural interface research, and benchmark results for pattern recognition and EMG-force applications. Data from 20 subjects were acquired twice per subject on different days following the same experimental paradigm. We acquired 256-channel HD-sEMG from forearm muscles during dexterous finger manipulations. This Hyser dataset contains five sub-datasets as: (1) pattern recognition (PR) dataset acquired during 34 commonly used hand gestures, (2) maximal voluntary muscle contraction (MVC) dataset while subjects contracted each individual finger, (3) one-degree of freedom (DoF) dataset acquired during force-varying contraction of each individual finger, (4) N-DoF dataset acquired during prescribed contractions of combinations of multiple fingers, and (5) random task dataset acquired during random contraction of combinations of fingers without any prescribed force trajectory. Dataset 1 can be used for gesture recognition studies. Datasets 2-5 also recorded individual finger forces, thus can be used for studies on proportional control of neuroprostheses. Our toolbox can be used to: (1) analyze each of the five datasets using standard benchmark methods and (2) decompose HD-sEMG signals into motor unit action potentials via independent component analysis. We expect our dataset, toolbox and benchmark analyses can provide a unique platform to promote a wide range of neural interface research and collaboration among neural rehabilitation engineers.

Superior Facilitation of an Action Observation Network by Congruent Character Movements in Brain-Computer Interface Action-Observation Games.

Action observation (AO) is a promising strategy for promoting motor function in neural rehabilitation. Recently, brain-computer interface (BCI)-AO game rehabilitation, which combines AO therapy with BCI technology, has been introduced to improve the effectiveness of rehabilitation. This approach can improve motor learning by providing feedback, which can be interactive in an observation task, and the game contents of the BCI-AO game paradigm can affect rehabilitation. In this study, the effects of congruent rather than incongruent feedback in a BCI-AO game on mirror neurons were investigated. Specifically, the mu suppression with congruent and incongruent BCI-AO games was measured in 17 healthy adults. The mu suppression in the central motor cortex was significantly higher with the congruent BCI-AO game than with the incongruent one. In addition, the satisfaction evaluation results were excellent for the congruent case. These results support the fact that providing feedback congruent with the motion of an action video facilitates mirror neuron activity and can offer useful guidelines for the design of BCI-AO games for rehabilitation.

Levodopa Improves Cognitive Function and the Deficits of Structural Synaptic Plasticity in Hippocampus Induced by Global Cerebral Ischemia/Reperfusion Injury in Rats.

The cognitive impairment caused by cerebral ischemia/reperfusion is an unsolved problem in the field of international neural rehabilitation. Not only ameliorates the consciousness level of certain patients who suffered from ischemia-reperfusion injury and were comatose for a long time period after cerebral resuscitation treatment, but levodopa also improves the symptoms of neurological deficits in rats with global cerebral ischemia-reperfusion injury. However, Levodopa has not been widely used as a brain protection drug after cardiopulmonary resuscitation, because of its unclear repair mechanism. Levodopa was used to study the neuroplasticity in the hippocampus of global cerebral ischemia/reperfusion injury rat model, established by Pulsinelli's four-vessel occlusion method. Levodopa was injected intraperitoneally at 50 mg/kg/d for 7 consecutive days after 1st day of surgery. The modified neurological function score, Morris water maze, magnetic resonance imaging, Nissl and TH staining, electron microscopy and western blot were used in the present study. The results showed that levodopa improved the neurological function and learning and memory of rats after global cerebral ischemia/reperfusion injury, improved the integrity of white matter, and density of gray matter in the hippocampus, increased the number of synapses, reduced the delayed neuronal death, and increased the expression of synaptic plasticity-related proteins (BDNF, TrkB, PSD95, and Drebrin) in the hippocampus. In conclusion, levodopa can improve cognitive function after global cerebral ischemia/reperfusion injury by enhancing the synaptic plasticity in the hippocampus.

Engagement Enhancement Based on Human-in-the-Loop Optimization for Neural Rehabilitation

Enhancing patients' engagement is of great benefit for neural rehabilitation. However, physiological and neurological differences among individuals can cause divergent responses to the same task, and the responses can further change considerably during training; both of these factors make engagement enhancement a challenge. This challenge can be overcome by training task optimization based on subjects' responses. To this end, an engagement enhancement method based on human-in-the-loop optimization is proposed in this paper. Firstly, an interactive speed-tracking riding game is designed as the training task in which four reference speed curves (RSCs) are designed to construct the reference trajectory in each generation. Each RSC is modeled using a piecewise function, which is determined by the starting velocity, transient time, and end velocity. Based on the parameterized model, the difficulty of the training task, which is a key factor affecting the engagement, can be optimized. Then, the objective function is designed with consideration to the tracking accuracy and the surface electromyogram (sEMG)-based muscle activation, and the physical and physiological responses of the subjects can consequently be evaluated simultaneously. Moreover, a covariance matrix adaption evolution strategy, which is relatively tolerant of both measurement noises and human adaptation, is used to generate the optimal parameters of the RSCs periodically. By optimization of the RSCs persistently, the objective function can be maximized, and the subjects' engagement can be enhanced. Finally, the performance of the proposed method is demonstrated by the validation and comparison experiments. The results show that both subjects' sEMG-based motor engagement and electroencephalography based neural engagement can be improved significantly and maintained at a high level.

Neurofeedback of scalp bi-hemispheric EEG sensorimotor rhythm guides hemispheric activation of sensorimotor cortex in the targeted hemisphere

Oscillatory electroencephalographic (EEG) activity is associated with the excitability of cortical regions. Visual feedback of EEG-oscillations may promote sensorimotor cortical activation, but its spatial specificity is not truly guaranteed due to signal interaction among interhemispheric brain regions. Guiding spatially specific activation is important for facilitating neural rehabilitation processes. Here, we tested whether users could explicitly guide sensorimotor cortical activity to the contralateral or ipsilateral hemisphere using a spatially bivariate EEG-based neurofeedback that monitors bi-hemispheric sensorimotor cortical activities for healthy participants. Two different motor imageries (shoulder and hand MIs) were selected to see how differences in intrinsic corticomuscular projection patterns might influence activity lateralization. We showed sensorimotor cortical activities during shoulder, but not hand MI, can be brought under ipsilateral control with guided EEG-based neurofeedback. These results are compatible with neuroanatomy; shoulder muscles are innervated bihemispherically, whereas hand muscles are mostly innervated contralaterally. We demonstrate the neuroanatomically-inspired approach enables us to investigate potent neural remodeling functions that underlie EEG-based neurofeedback via a BCI.

Trends in Compressive Sensing for EEG Signal Processing Applications.

The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain–computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.

Exploiting evolutionarily conserved pathways to promote plasticity of human spinal circuits.

Humans evolved from species that walked on all 4 limbs, which means that experiments in quadrupeds can guide and support experiments in humans. This is particularly helpful for neural rehabilitation because the central nervous system is plastic in nature, meaning that activities promoting central nervous system activity can alter subsequent output properties. This is known as neuroplasticity and can be measured as changes in spinal cord excitability through reflexes as a proxy. By targeting evolutionarily conserved pathways that act on similar interneurons within the spinal cord to either increase or decrease excitability, it may be possible to preferentially modulate spinal cord excitability based on a desirable outcome. For example, rhythmic movement reduces spinal cord excitability whereas brief sensory input to cutaneous afferents increases spinal cord excitability. Alterations in spinal cord excitability have been shown to outlast the activity duration, suggesting that neuroplasticity is not transient. This evidence suggests that both rhythmic movement and sensory input can induce acute neuroplasticity of spinal cord excitability. The overall purpose of this dissertation was 2-fold: (i) to provide reviews of how evolutionarily conserved pathways are studied in humans and how they contribute to human rhythmic movement; and (ii) experimentally examine how these conserved pathways, which converge onto similar interneuron circuitry, can be exploited to cause bidirectional changes in spinal cord excitability. Reviews indicate that humans have retained characteristics of quadrupedal locomotion and, in particular, activity of the arms affects the excitability of the legs, and vice versa. Cutaneous input is integrated throughout the body during locomotion, such that cutaneous sensations elicit neuromechanical responses that are nerve-specific and modulated according to the phase of movement. In experiment 1, there was increased spinal cord excitability following patterned stimulation of cutaneous afferents innervating the bottom of the foot. In experiment 2, stimulation to cutaneous afferents innervating both the top and bottom of the foot amplified voluntary plantar- and dorsiflexion. In experiment 3, cervicolumbar connections were exploited to amplify plasticity in spinal cord excitability induced by rhythmic movement. Finally, in experiment 4, there were interactions of rhythmic movement and fatigue, which both reduce spinal cord excitability, with cutaneous stimulation, which increases spinal cord excitability, such that reductions in spinal cord excitability associated with fatigue were mitigated by cutaneous stimulation. Taken together, these experiments suggest that cutaneous stimulation can increase spinal cord excitability, whereas quadrupedal locomotor activity can decrease spinal cord excitability. These conserved pathways can be exploited to intentionally modify spinal cord excitability in a bidirectional fashion, which provides fruitful information for the exploration of rehabilitation and sport performance practices.

Toward Improving Engagement in Neural Rehabilitation: Attention Enhancement Based on Brain–Computer Interface and Audiovisual Feedback

Both motor and cognitive function rehabilitation benefits can be improved significantly by patients’ active participation. To this goal, an attention enhancement system based on the brain–computer interface (BCI) and audiovisual feedback is proposed. First, an interactive position-tracking riding game is designed to increase the training challenge and neural engagement. Subjects were asked to drive one of the avatars to keep up with another by adjusting their riding speed and attention. Second, the subject’s electroencephalogram (EEG)-based attention level is divided into three regions (low, moderate, and high) by using the theta-to-beta ratio (TBR). According to the subject’s attention states, different speed adjustment strategies are adopted to adjust the tracking challenge and improve the subject’s attention. Besides, if the subject’s attention focused on the training is moderate or low, an auditory feedback will be given to remind the subject to pay more attention to the training. The contrast experimental results show that subjects’ performance indicated by overall attention level and average muscle activation can be improved significantly by using the attention enhancement system, which validates the feasibility of the proposed system for improving the neural and motor engagement.

An Exploratory Randomized Trial of Physical Therapy for the Treatment of Chemotherapy-Induced Peripheral Neuropathy

Background. Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of taxane treatment and cannot currently be prevented or adequately treated. Physical therapy is often used for neural rehabilitation following injury but has not been evaluated in this patient population. Methods. Single-blind, randomized controlled exploratory study compared standard care to a physical therapy home program (4 visits) throughout adjuvant taxane chemotherapy for stage I-III patients with breast cancer (n = 48). Patient questionnaires and quantitative sensory testing evaluated the treatment effect throughout chemotherapy to 6 months post treatment. Nonrandomized subgroup analysis observed effect of general exercise on sensory preservation comparing those reporting moderate exercise throughout chemotherapy to those that did not exercise regularly. Clinical Trial Registration. clinicaltrials.gov (NCT02239601). Results. The treatment group showed strong trends toward less pain (odds ratio [OR] 0.41, 95% confidence interval [CI] 0.17-1.01; P = .053) and pain decreased over time (OR 0.85, 95% CI 0.76-0.94; P = .002). Pain pressure thresholds (P = .034) and grip dynamometry (P < .001) were improved in the treatment group. For the nonrandomized subgroup analysis, participants reporting general exercise had preservation of vibration (Left P = .001, Right P = .001) and normal heat pain thresholds (Left P = .021, Right P = .039) compared with more sedentary participants. Conclusion. Physical therapy home program may improve CIPN pain in the upper extremity for patients with breast cancer, and general exercise throughout chemotherapy treatment was observed to have correlated to preservation of sensory function. Further research is required to confirm the impact of a physical therapy home program on CIPN symptoms.

Current status of infarction in the basal ganglia-internal capsule due to mild head injury in children using PRISMA guidelines.

Post-traumatic basal ganglia-internal capsule (BGIC) infarction in pediatric patients is a relatively rare consequence of mild head injury (MHI). To the best of the authors' knowledge, at present, no comprehensive review has been published. To review research on BGIC infarction after MHI, a literature search was performed using the PubMed database and relevant search terms. According to recent data, MHI may cause BGIC infarction due to mechanical vasospasm of the perforating vessels in pediatric patients. The anatomical characteristics of the growing brain in infancy, mineralization of the lenticulostriate arteries and viral infection may all play a part in BGIC infarction after MHI, which often occurs within 24 months. Symptoms are not as severe and tend to disappear in the early period. Computed tomography or magnetic resonance imaging often shows BGIC infarction. There are also children with scattered calcification of the basal ganglia. Neural rehabilitation is a commonly accepted treatment. The prognosis of patients with BGIC infarction after MHI consistently improves.

Optimal Design and Experimental Assessment of a Wireless Power Transfer System for Home-Cage Monitoring

Techniques of long-term in vivo electrophysiological recording play important roles in brain research and neural rehabilitation. To avoid interruption of experiment and risk of infection, use of wireless power transfer (WPT) technique has been suggested to eliminate cumbersome wires and batteries attached to the animals in rodent electrophysiological applications. This paper presents a holistic assessment of the relationships among the physical sizes of the transmitting and receiving coils, power transfer characteristics, and specific absorption rate (SAR) in animals of a simple WPT system using two rectangular coaxial transmitting coils. With given space for the animal and size of the receiving coil, a procedure for designing the minimum driving current, and the transmitting coil dimensions and separation to deliver sufficient power to the receiver, and interactions between the transmitting and receiving coils is derived. A π -capacitor network that can match the impedances of the receiving coil and the load to operate the receiver at the maximum power transfer condition is proposed. It is also optimized for the overall volume. A 100-mW prototype with an operating zone of 400 × 240 × 40 mm3 and a receiving coil with a diameter of 11.45 mm is built and studied. The SAR in the animal is evaluated and compared with the recommended restriction level.

Study on brain computer interface combined tactile enhancement and time-varying features.

Neuroplasticity plays an important role in the recovery of injured nervous system. Both motor imagery (MI) and functional electrical stimulation (FES) can promote plasticity by activating the sensorimotor cortex. Specifically, MI as control strategy to activate FES in a brain computer interface (BCI) is a promising approach for motor functions recovery. This study demonstrated the efficiency of somatosensory input provided by electrical stimulation (ES) on cortical activation during MI. And the performance of classifiers with time-varying electroencephalography (EEG) features also be probed. We inspected the cortical activation by EEG for three experiment conditions, i.e. ES during MI, MI and ES. And the classification accuracy of three conditions were discussed respectively. Results showed that the ES during MI could induce stronger cortical activation than the other two conditions, and the classifier with time-varying EEG features had a higher classification accuracy. The results demonstrated that MI-based BCI combined MI and ES which fulfills two properties of somatosensory input and time-varying features is an available approach for motor neural rehabilitation.

Design of an Active and Passive Control System of Hand Exoskeleton for Rehabilitation

Aiming at stroke patients’ hand rehabilitation training, we present a hand exoskeleton with both active and passive control modes for neural rehabilitation. The exoskeleton control system is designed as a human–robot interaction control system based on field-programmable gate array (FPGA) and Android mobile terminal with good portability and openness. Passive rehabilitation pattern based on proportional derivative (PD) inverse dynamic control method and active rehabilitation pattern based on impedance method, are established respectively. By the comparison of the threshold value and the force on the fingertip of the exoskeleton from the sensor, the automatic switch between active and passive rehabilitation mode is accomplished. The hand model is built in Android environment that can synchronize the movement of the hand. It can also induce patients to participate in rehabilitation training actively. To verify the proposed control approach, we set up and conduct an experiment to do the passive rehabilitation mode, active rehabilitation mode, and active plus passive mode experimental researches. The experiment results effectively verify the feasibility of the exoskeleton system fulfilling the proposed control strategy.

A hybrid BCI system based on motor imagery and transient visual evoked potential

Motion imaging (MI) refers to the psychological realization of motions without movement or muscle activity; the basis of neural rehabilitation as a brain-computer interface (BCI) technique has been extensively studied. The combination of motor imaging and brain-computer interface technology can take advantage of patients’ willingness to take the initiative to assist them in rehabilitation. Studies have shown that MI combined with BCI rehabilitation training is better than traditional rehabilitation training. Transient visual evoked potentials and motor imaging constructed a hybrid BCI system. Three healthy subjects were tested. EEG signals were superimposed preprocessing according to visual stimulus superimposed frequency and motor guidance frequency respectively. Transient visual evoked EEG segmentation is used as a control signal of choice, the use of wavelet decomposition helps to extract features, and then use BP neural network recognition for classification and identification. Visual guidance, motion-oriented event-related synchronization, or desynchronization feature signals as rehabilitation exercise control signals, are using time-domain sliding energy analysis to extract features, and then using BP neural network recognition for classification and identification. EEG signals collected in the experiment were superimposed signals of transient visual evoked and motorized EEG. There were 300 transient electroencephalogram (EEG) and 100 segments Imagine EEG segmentation. According to the results of the test, the average recognition rate of visual evoked EEG reached 95.42%; the average recognition rate of motor imaginary EEG was 73.08%, but there was a large individual difference in motor imaging EEG signals except 1 Name of the test rate of 85%, the remaining two subjects were less than 70% recognition rate. There is a large individual difference between motion imaging and signal feature recognition, and it takes a long time to train. Therefore, it is necessary to study further the selection of control signals for rehabilitation training. As the threshold feedback signal, controlling the amplitude feedback of rehabilitation training can promote the motivation of participants’ motivation to stimulate and enhance the rehabilitation treatment effect.

Fusing Frontal and Occipital EEG Features to Detect “Brain Switch” by Utilizing Convolutional Neural Network

Flicker is the most widely used steady-state visual evoked potential (SSVEP) stimulus. In addition, checkerboard can induce steady-state motion visual evoked potential (SSMVEP) in the occipital area. More recently, the action video is proposed to simultaneously elicit SSMVEP and induce sensorimotor area activations via the mirror neuron systems through Action Observation (AO). Integration of AO with brain–computer interface (BCI) is appealing for neural rehabilitation applications. In order to make such a BCI paradigm more feasible in neural rehabilitation, it is essential to discriminate whether a user is actively engaged with the BCI, i.e. intentional control (IC) state, or not engaged, i.e. non-intentional control (NC) state. In this study, the EEG responses to these three types of visual stimuli were compared for the first time and a convolutional neural network (CNN) was proposed to discriminate IC and NC states. A visual gaiting stimulus was designed to realize BCI-based AO. The results showed that the power of alpha rhythms from frontal area decreased more when the participants engaged at the gaiting stimuli than when the participants engaged at the other two types of stimuli. In addition, the correlation coefficient between the EEG from the occipital area and the template signals increased when the participants engaged at the stimuli. The results also clearly demonstrated the proposed CNN method can discriminate the IC and NC states. In addition, the combination of the attention feature from the frontal area and the SSVEP/SSMVEP feature from the occipital area showed significant performance improvement for the gaiting stimulus, but not for the other two types of stimuli.

Prospects of Electrooculography in Human-Computer Interface Based Neural Rehabilitation for Neural Repair Patients

Disable persons are facing a lot of problems in daily life activities. They need some help from others to fulfil their needs every day. To avoid this condition modern technology help such persons to overcome the problem in a natural way like bio signal based-human–computer interaction. In this paper, we focused to study the performance of male subjects compared with female subjects to analyze the performance to design Electrooculographgy-based HCI using periodogram and neural network. Five male subjects and five female subjects are involved in this experiment. From the experimental analysis, we identified that male performance was maximum compared to female performance. From this paper, we analyzed that subject S4 from male subjects and subject S10 from female subjects performance was marginally high compared with other subjects performance took part in this experiment. From the classification accuracy, we conclude that male subject performance was encouraged with 93.67 % and 92.28% for female subjects. The offline test was conducted in the indoor environment to identify the tasks to confirm the performance of individual subjects. From the offline analysis, we conclude that subject S4 performance was high compared to other subjects take part in this paper. Subject S4 took less time to perform the task as per the protocol. Through this paper, we confirm that scheming HCI is achievable.