Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review

Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brain–computer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go

Brain computer interfaces: an engineering view. Design, implementation and test of a SSVEP-based BCI.

This thesis presents the realization of a compact, yet flexible BCI platform, which, when compared to most commercially-available solution, can offer an optimal trade-off between the following requirements: (i) minimal, easy experimental setup; (ii) flexibility, allowing simultaneous studies on other bio-potentials; (iii) cost effectiveness (e.g. < 1000 €); (iv) robust design, suitable for operation outside lab environments. The thesis encompasses all the project phases, from hardware design and realization, up to software and signal processing. The work started from the development of the hardware acquisition unit. It resulted in a compact, battery-operated module, whose medium-to-large scale production costs are in the range of 300 €. The module features 16 input channels and can be used to acquire different bio-potentials, including EEG, EMG, ECG. Module performance is very good (RTI noise < 1.3 uVpp), and was favourably compared against a commercial device (g.tec USBamp). The device was integrated into an ad-hoc developed Matlab-based platform, which handles the hardware control, as well as the data streaming, logging and processing. Via a specifically developed plug-in, incoming data can also be streamed to a TOBI-interface compatible system. As a demonstrator, the BCI was developed for AAL (Ambient Assisted Living) system-control purposes, having in mind the following requirements: (i) online, self-paced BCI operation (i.e., the BCI monitors the EEG in real-time and must discern between intentional control periods, and non-intentional, rest ones, interpreting the user’s intent only in the first case); (ii) calibration-free approach (“ready-to-use”, “Plug&Play”); (iii) subject-independence (general approach). The choice of the BCI operating paradigm fell on Steady State visual Evoked Potential (SSVEP). Two offline SSVEP classification algorithms were proposed and compared against reference literature, highlighting good performance, especially in terms of lower computational complexity. A method for improving classification accuracy was presented, suitable for use in online, self-paced scenarios (since it can be used to discriminate between intentional control periods and non-intentional ones). Results show a very good performance, in particular in terms of false positives immunity (0.26 min^-1), significantly improving over the state of the art. The whole BCI setup was tested both in lab condition, as well as in relatively harsher ones (in terms of environmental noise and non-idealities), such as in the context of the Handimatica 2014 exhibition. In both cases, a demonstrator allowing control of home appliances through BCI was developed

Implementing physiologically-based approaches to improve Brain-Computer Interfaces usability in post-stroke motor rehabilitation

Stroke is one of the leading causes of long-term motor disability and, as such, directly impacts on daily living activities. Identifying new strategies to recover motor function is a central goal of clinical research. In the last years the approach to the post-stroke function restore has moved from the physical rehabilitation to the evidence-based neurological rehabilitation. Brain-Computer Interface (BCI) technology offers the possibility to detect, monitor and eventually modulate brain activity. The potential of guiding altered brain activity back to a physiological condition through BCI and the assumption that this recovery of brain activity leads to the restoration of behaviour is the key element for the use of BCI systems for therapeutic purposes. To bridge the gap between research-oriented methodology in BCI design and the usability of a system in the clinical realm requires efforts towards BCI signal processing procedures that would optimize the balance between system accuracy and usability. The thesis focused on this issue and aimed to propose new algorithms and signal processing procedures that, by combining physiological and engineering approaches, would provide the basis for designing more usable BCI systems to support post-stroke motor recovery. Results showed that introduce new physiologically-driven approaches to the pre-processing of BCI data, methods to support professional end-users in the BCI control parameter selection according to evidence-based rehabilitation principles and algorithms for the parameter adaptation in time make the BCI technology more affordable, more efficient, and more usable and, therefore, transferable to the clinical realm

Development of Brain Machine Interface Systems and its Applications to Prosthetic Hand Control

東京電機大学201

Personalized Health Monitoring Using Evolvable Block-based Neural Networks

This dissertation presents personalized health monitoring using evolvable block-based neural networks. Personalized health monitoring plays an increasingly important role in modern society as the population enjoys longer life. Personalization in health monitoring considers physiological variations brought by temporal, personal or environmental differences, and demands solutions capable to reconfigure and adapt to specific requirements. Block-based neural networks (BbNNs) consist of 2-D arrays of modular basic blocks that can be easily implemented using reconfigurable digital hardware such as field programmable gate arrays (FPGAs) that allow on-line partial reorganization. The modular structure of BbNNs enables easy expansion in size by adding more blocks. A computationally efficient evolutionary algorithm is developed that simultaneously optimizes structure and weights of BbNNs. This evolutionary algorithm increases optimization speed by integrating a local search operator. An adaptive rate update scheme removing manual tuning of operator rates enhances the fitness trend compared to pre-determined fixed rates. A fitness scaling with generalized disruptive pressure reduces the possibility of premature convergence. The BbNN platform promises an evolvable solution that changes structures and parameters for personalized health monitoring. A BbNN evolved with the proposed evolutionary algorithm using the Hermite transform coefficients and a time interval between two neighboring R peaks of ECG signal, provides a patient-specific ECG heartbeat classification system. Experimental results using the MIT-BIH Arrhythmia database demonstrate a potential for significant performance enhancements over other major techniques

Electroencephalography (EEG)-based brain computer interfaces for rehabilitation

Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of previously designed 2D cursor control BCI, and the feasibility of the prototype is tested in electroencephalography (EEG) experiments; guidance on enhancing system performance is provided by a simulation incorporating intelligent control approaches under different EEG decoding accuracies; pattern recognition methods are explored to provide optimized classification results; and a hybrid BCI system is built to enhance the usability of the wheelchair BCI system. Methods: In the virtual wheelchair control study, a creative and user friendly control strategy was proposed, and a paradigm was designed in Matlab, providing a virtual environment for control experiments; five subjects performed physical/imagined left/right hand movements or non-control tasks to control the virtual wheelchair to move forward, turn left/right or stop; 2-step classification methods were employed and the performance was evaluated by hit rate and control time. Feature analysis and time-frequency analysis were conducted to examine the spatial, temporal and frequency properties of the utilized SMR features, i.e. event-related desynchronization (ERD) and post-movement event-related synchronization (ERS). The simulation incorporated intelligent control methods, and evaluated navigation and positioning performance with/without obstacles under different EEG decoding accuracies, to better guide optimization. Classification methods were explored considering different feature sets, tuned classifier parameters and the simulation results, and a recommendation was provided to the proposed system. In the steady state visual evoked potential (SSVEP) system for hybrid BCI study, a paradigm was designed, and an electric circuit system was built to provide visual stimulus, involving SSVEP as another type of signal being used to drive the EEG BCI system. Experiments were conducted and classification methods were explored to evaluate the system performance. Results: ERD was observed on both hemispheres during hand\u27s movement or motor imagery; ERS was observed on the contralateral hemisphere after movement or motor imagery stopped; five subjects participated in the continuous 2D virtual wheelchair control study and 4 of them hit the target with 100% hit rate in their best set with motor imagery. The simulation results indicated that the average hit rate with 10 obstacles can get above 95% for pass-door tests and above 70% for positioning tests, with EEG decoding accuracies of 70% for Non-Idle signals and 80% for idle signals. Classification methods showed that with properly tuned parameters, an average of about 70%-80% decoding accuracy for all the classifiers could be reached, which reached the requirements set by the simulation test. Initial test on the SSVEP BCI system exhibited high classification accuracy, which may extend the usability of the wheelchair system to a larger population when finally combined with ERD/ERS BCI system. Conclusion: This research investigated the feasibility of using both ERD and ERS associated with natural hand\u27s motor imagery, aiming to implement practical BCI systems for the end users in the rehabilitation stage. The simulation with intelligent controls provided guides and requirements for EEG decoding accuracies, based on which pattern recognition methods were explored; properly selected features and adjusted parameters enabled the classifiers to exhibit optimal performance, suitable for the proposed system. Finally, to enlarge the population for which the wheelchair BCI system could benefit for, a SSVEP system for hybrid BCI was designed and tested. These systems provide a non-invasive, practical approach for BCI users in controlling assistive devices such as a virtual wheelchair, in terms of ease of use, adequate speed, and sufficient control accuracy

Sound-production Related Cognitive Tasks for Onset Detection in Self-Paced Brain-Computer Interfaces

Objective. The main goal of this research is proposing a novel method of onset detection for Self-Paced (SP) Brain-Computer Interfaces (BCIs) to increase usability and practicality of BCIs towards real-world uses from laboratory research settings. Approach. To achieve this goal, various Sound-Production Related Cognitive Tasks (SPRCTs) were tested against idle state in offline and simulated-online experiments. An online experiment was then conducted that turned a messenger dialogue on when a new message arrived by executing the Sound Imagery (SI) onset detection task in real-life scenarios (e.g. watching video, reading text). The SI task was chosen as an onset task because of its advantages over other tasks: 1) Intuitiveness. 2) Beneficial for people with motor disabilities. 3) No significant overlap with other common, spontaneous cognitive states becoming easier to use in daily-life situations. 4) No dependence on user’s mother language. Main results. The final online experimental results showed the new SI onset task had significantly better performance than the Motor Imagery (MI) approach. 84.04% (SI) vs 66.79% (MI) TFP score for sliding image scenario, 80.84% vs 61.07% for watching video task. Furthermore, the onset response speed showed the SI task being significantly faster than MI. In terms of usability, 75% of subjects answered SI was easier to use. Significance. The new SPRCT outperforms typical MI for SP onset detection BCIs (significantly better performance, faster onset response and easier usability), therefore it would be more easily used in daily-life situations. Another contribution of this thesis is a novel EMG artefact-contaminated EEG channel selection and handling method that showed significant class separation improvement against typical blind source separation techniques. A new performance evaluation metric for SP BCIs, called true-false positive score was also proposed as a standardised performance assessment method that considers idle period length, which was not considered in other typical metrics