Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming and artificial intelligence. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and nonlinear brain dynamics and related feature extraction and classification challenges. Psycho-neurophysiological phenomena and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes progress in BCI field and highlights critical challenges.

Progress in Brain Computer Interfaces: Challenges and Trends.

Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.

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Progress in Brain Computer Interface: Challenges and Opportunities.

The Fugl-Meyer assessment (FMA) is the most popular instrument for evaluating upper extremity motor function in stroke patients However, it is a labor-intensive and time-consuming method This paper proposes a novel automated FMA system to overcome these limitations of the FMA For automation, we used Kinect v2 and force sensing resistor sensors owing to their convenient installation as compared with body-worn sensors Based on the linguistic guideline of the FMA, a rule-based binary logic classification algorithm was developed to assign FMA scores using the extracted features obtained from the sensors The algorithm is appropriate for clinical use, because it is not based on machine learning, which requires additional learning processes with a large amount of clinical data The proposed system was able to automate 79% of the FMA tests because of optimized sensor selection and the classification algorithm In clinical trials conducted with nine stroke patients, the proposed system exhibited high scoring accuracy (92%) and time efficiency (85% reduction in clinicians’ required time)

Automated Evaluation of Upper-Limb Motor Function Impairment Using Fugl-Meyer Assessment

A systematic review on hybrid EEG/fNIRS in brain-computer interface

Event-related desynchronization and synchronization (ERD/S) and movement-related cortical potential (MRCP) play an important role in brain-computer interfaces (BCI) for lower limb rehabilitation, particularly in standing and sitting. However, little is known about the differences in the cortical activation between standing and sitting, especially how the brain’s intention modulates the pre-movement sensorimotor rhythm as they do for switching movements. In this study, we aim to investigate the decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for the actions of standing and sitting. We developed a behavioral task in which participants were instructed to perform both AO and MI/ME in regard to the transitioning actions of sit-to-stand and stand-to-sit. Our results demonstrated that the ERD was prominent during AO, whereas ERS was typical during MI at the alpha band across the sensorimotor area. A combination of the filter bank common spatial pattern (FBCSP) and support vector machine (SVM) for classification was used for both offline and classifier testing analyses. The offline analysis indicated the classification of AO and MI providing the highest mean accuracy at 82.73±2.54% in the stand-to-sit transition. By applying the classifier testing analysis, we demonstrated the higher performance of decoding neural intentions from the MI paradigm in comparison to the ME paradigm. These observations led us to the promising aspect of using our developed tasks based on the integration of both AO and MI to build future exoskeleton-based rehabilitation systems.

Decoding EEG Rhythms During Action Observation, Motor Imagery, and Execution for Standing and Sitting

Enhancing motor imagery (MI) results in amplified event-related desynchronization (ERD) and is important for MI-based rehabilitation and brain–computer interface (BCI) applications. Many attempts to enhance the MI by providing a visual guidance have been reported. We believe that the rubber hand illusion (RHI), which induces body ownership over an external object, can provide better guidance to enhance MI; thus, an RHI-based paradigm with motorized moving rubber hand was proposed. To validate the proposed MI enhancing paradigm, we conducted an experimental comparison among paradigms with 20 healthy subjects. The peak amplitude and arrival times of ERD were compared at contralateral and ipsilateral electroencephalogram channels. We found significantly amplified ERD caused by the proposed paradigm, which is similar to the ERD caused by motor execution. In addition, the arrival time suggests that the proposed paradigm is applicable for BCI. In conclusion, the proposed paradigm can significantly enhance the MI with better characteristics for use with BCI.

A Paradigm to Enhance Motor Imagery Using Rubber Hand Illusion Induced by Visuo-Tactile Stimulus

Goal: For healthcare and clinical use, ambulatory gait monitoring systems using inertial sensors have been developed to estimate the user gait parameters, such as walking speed, stride time, and stride length. However, to adapt the systems effectively to daily-life activities, they need to be able to classify the gait activities of daily-life to obtain the parameters for each activity. In this study, we propose a simple classification algorithm based on a single inertial sensor for ease of use, which classifies three major gait activities: leveled walk, ramp walk, and stair walk. Method: The classification can be performed with gait parameter estimation simultaneously. The developed system that includes classification and parameter estimation algorithms was evaluated with eight healthy subjects within a gait lab and on an outdoor daily-life walking course. Results: The results showed that the estimated gait parameters were comparable to existing studies (range of walking speed root mean square error: 0.059–0.129 m/s), and the classification accuracy was sufficiently high for all three gait activities: 98.5% for the indoor gait lab experiment and 95.5% for the outdoor complex daily-life walking course experiment. Conclusion: The proposed system is simple and effective for daily-life gait analysis, including gait activity classification and gait parameter estimation for each activity.

An Ambulatory Gait Monitoring System with Activity Classification and Gait Parameter Calculation Based on a Single Foot Inertial Sensor

This paper is to develop an automated assessment system of upper-limb motor function impairment for clinical environment. Although we had proposed the system in our previous work, there are some rooms to be improved. Using glove sensor was difficult due to stroke patient's hand contracture. Moreover, it was based on machine learning, and thus required huge effort to collect reference data to increase classification accuracy. To address those issues, three tests of Fugl-Meyer Assessment which were closely related the issues were chosen as target tests. Since Kinect v2 and force-sensing resister can provide hand-related information, the tests were automated without glove sensor. Fuzzy-logic classification table that is based on traditional FMA guidelines was implemented to rate the FMA score without machine learning. With a healthy subject, simple experiments were conducted to evaluate the proposed system with novel classification scheme. The results show a feasibility for more convenient automated assessment of upper-limb motor function impairment.

Towards clinically relevant automatic assessment of upper-limb motor function impairment

Recently, motor imagery brain-computer interface (MI-BCI) has been studied as a motor learning method and evaluated by comparing with conventional passive and active training. Most functional near-infrared spectroscopy (fNIRS) studies adopted block design for comparing those motor learning methods, including MI-BCI. Compared to the block design, event-related design would be more appropriate for estimating cortical activation in MI-BCI which provides feedback for each trial. This paper is a preliminary study to check the feasibility whether event-related design can be applicable for MI-BCI. To this end, three different motor learning methods involving MI-BCI were compared. In hemodynamic response, MI-BCI showed significantly stronger cortical activation than passive training (PT), and weaker than active training (AT), which conforms most existing studies. The results demonstrate that event-related design could be applied to investigate cortical effects of MI-BCI and comparing hemodynamic responses of different motor learning methods.

Toward Comparison of Cortical Activation with Different Motor Learning Methods Using Event-Related Design: EEG-fNIRS Study

In brain-computer interface based on motor imagery for rehabilitation, false positive could be a major cause of undesired brain plasticity which ends up with the wrong reconstruction of damaged brain tracts. Moreover, the number of electroencephalogram (EEG) electrodes required would be the reason of practical difficulties to clinical use. To reduce the false positive and the number of electrodes required, we proposed a novel two-phase classifier based on detecting Mu band event-related desynchronization (ERD). Along with five channels to detect motor imagery, the algorithm only uses three channels to reject ERD-like noise or non-motor signals. The performance of the proposed algorithm was evaluated through two-day experiments with four healthy subjects. The total sensitivity was 60.83% and the total selectivity was 78.49%. Those experimental results show that the proposed method can reduce the rate of false positives with small number of EEG channels.

A Novel Movement Intention Detection Method for Neurorehabilitation Brain-Computer Interface System

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