Continuous 2D control via state-machine triggered by endogenous sensory discrimination and a fast brain switch

Detection of Movement-Related Brain Activity Associated with Hand and Tongue Movements from Single-Trial Around-Ear EEG

Movement intentions of motor impaired individuals can be detected in laboratory settings via electroencephalography Brain-Computer Interfaces (EEG-BCIs) and used for motor rehabilitation and external system control. The real-world BCI use is limited by the costly, time-consuming, obtrusive, and uncomfortable setup of scalp EEG. Ear-EEG offers a faster, more convenient, and more aesthetic setup for recording EEG, but previous work using expensive amplifiers detected motor intentions at chance level. This study investigates the feasibility of a low-cost ear-EEG BCI for the detection of tongue and hand movements for rehabilitation and control purposes. In this study, ten able-bodied participants performed 100 right wrist extensions and 100 tongue-palate movements while three channels of EEG were recorded around the left ear. Offline movement vs. idle activity classification of ear-EEG was performed using temporal and spectral features classified with Random Forest, Support Vector Machine, K-Nearest Neighbours, and Linear Discriminant Analysis in three scenarios: Hand (rehabilitation purpose), hand (control purpose), and tongue (control purpose). The classification accuracies reached 70%, 73%, and 83%, respectively, which was significantly higher than chance level. These results suggest that a low-cost ear-EEG BCI can detect movement intentions for rehabilitation and control purposes. Future studies should include online BCI use with the intended user group in real-life settings.

Considerations and discussions on the clear definition and definite scope of brain-computer interfaces

Brain-computer interface (BCI) is a revolutionizing human-computer interaction with potential applications in both medical and non-medical fields, emerging as a cutting-edge and trending research direction. Increasing numbers of groups are engaging in BCI research and development. However, in recent years, there has been some confusion regarding BCI, including misleading and hyped propaganda about BCI, and even non-BCI technologies being labeled as BCI. Therefore, a clear definition and a definite scope for BCI are thoroughly considered and discussed in the paper, based on the existing definitions of BCI, including the six key or essential components of BCI. In the review, different from previous definitions of BCI, BCI paradigms and neural coding are explicitly included in the clear definition of BCI provided, and the BCI user (the brain) is clearly identified as a key component of the BCI system. Different people may have different viewpoints on the definition and scope of BCI, as well as some related issues, which are discussed in the article. This review argues that a clear definition and definite scope of BCI will benefit future research and commercial applications. It is hoped that this review will reduce some of the confusion surrounding BCI and promote sustainable development in this field.

Electrotactile BCI for Top-Down Somatosensory Training: Clinical Feasibility Trial of Online BCI Control in Subacute Stroke Patients

This study investigates the feasibility of a novel brain-computer interface (BCI) device designed for sensory training following stroke. The BCI system administers electrotactile stimuli to the user's forearm, mirroring classical sensory training interventions. Concurrently, selective attention tasks are employed to modulate electrophysiological brain responses (somatosensory event-related potentials-sERPs), reflecting cortical excitability in related sensorimotor areas. The BCI identifies attention-induced changes in the brain's reactions to stimulation in an online manner. The study protocol assesses the feasibility of online binary classification of selective attention focus in ten subacute stroke patients. Each experimental session includes a BCI training phase for data collection and classifier training, followed by a BCI test phase to evaluate online classification of selective tactile attention based on sERP. During online classification tests, patients complete 20 repetitions of selective attention tasks with feedback on attention focus recognition. Using a single electroencephalographic channel, attention classification accuracy ranges from 70% to 100% across all patients. The significance of this novel BCI paradigm lies in its ability to quantitatively measure selective tactile attention resources throughout the therapy session, introducing a top-down approach to classical sensory training interventions based on repeated neuromuscular electrical stimulation.

A Systematic Review of Virtual Reality and Robot Therapy as Recent Rehabilitation Technologies Using EEG-Brain-Computer Interface Based on Movement-Related Cortical Potentials

To enhance the treatment of motor function impairment, patients' brain signals for self-control as an external tool may be an extraordinarily hopeful option. For the past 10 years, researchers and clinicians in the brain-computer interface (BCI) field have been using movement-related cortical potential (MRCP) as a control signal in neurorehabilitation applications to induce plasticity by monitoring the intention of action and feedback. Here, we reviewed the research on robot therapy (RT) and virtual reality (VR)-MRCP-based BCI rehabilitation technologies as recent advancements in human healthcare. A list of 18 full-text studies suitable for qualitative review out of 322 articles published between 2000 and 2022 was identified based on inclusion and exclusion criteria. We used PRISMA guidelines for the systematic review, while the PEDro scale was used for quality evaluation. Bibliometric analysis was conducted using the VOSviewer software to identify the relationship and trends of key items. In this review, 4 studies used VR-MRCP, while 14 used RT-MRCP-based BCI neurorehabilitation approaches. The total number of subjects in all identified studies was 107, whereby 4.375 ± 6.3627 were patient subjects and 6.5455 ± 3.0855 were healthy subjects. The type of electrodes, the epoch, classifiers, and the performance information that are being used in the RT- and VR-MRCP-based BCI rehabilitation application are provided in this review. Furthermore, this review also describes the challenges facing this field, solutions, and future directions of these smart human health rehabilitation technologies. By key items relationship and trends analysis, we found that motor control, rehabilitation, and upper limb are important key items in the MRCP-based BCI field. Despite the potential of these rehabilitation technologies, there is a great scarcity of literature related to RT and VR-MRCP-based BCI. However, the information on these rehabilitation methods can be beneficial in developing RT and VR-MRCP-based BCI rehabilitation devices to induce brain plasticity and restore motor impairment. Therefore, this review will provide the basis and references of the MRCP-based BCI used in rehabilitation applications for further clinical and research development.

Manual 3D Control of an Assistive Robotic Manipulator Using Alpha Rhythms and an Auditory Menu: A Proof-of-Concept

Brain–Computer Interfaces (BCIs) have been regarded as potential tools for individuals with severe motor disabilities, such as those with amyotrophic lateral sclerosis, that render interfaces that rely on movement unusable. This study aims to develop a dependent BCI system for manual end-point control of a robotic arm. A proof-of-concept system was devised using parieto-occipital alpha wave modulation and a cyclic menu with auditory cues. Users choose a movement to be executed and asynchronously stop said action when necessary. Tolerance intervals allowed users to cancel or confirm actions. Eight able-bodied subjects used the system to perform a pick-and-place task. To investigate the potential learning effects, the experiment was conducted twice over the course of two consecutive days. Subjects obtained satisfactory completion rates (84.0 ± 15.0% and 74.4 ± 34.5% for the first and second day, respectively) and high path efficiency (88.9 ± 11.7% and 92.2 ± 9.6%). Subjects took on average 439.7 ± 203.3 s to complete each task, but the robot was only in motion 10% of the time. There was no significant difference in performance between both days. The developed control scheme provided users with intuitive control, but a considerable amount of time is spent waiting for the right target (auditory cue). Implementing other brain signals may increase its speed.

Feature- and classification analysis for detection and classification of tongue movements from single-trial pre-movement EEG

Individuals with severe tetraplegia can benefit from brain-computer interfaces (BCIs). While most movement-related BCI systems focus on right/left hand and/or foot movements, very few studies have considered tongue movements to construct a multiclass BCI. The aim of this study was to decode four movement directions of the tongue (left, right, up, and down) from single-trial pre-movement EEG and provide a feature and classifier investigation. In offline analyses (from ten healthy participants) detection and classification were performed using temporal, spectral, entropy, and template features classified using either a linear discriminative analysis, support vector machine, random forest or multilayer perceptron classifiers. Besides the 4-class classification scenario, all possible 3-, and 2-class scenarios were tested to find the most discriminable movement type. The linear discriminant analysis achieved on average, higher classification accuracies for both movement detection and classification. The right- and down tongue movements provided the highest and lowest detection accuracy (95.3±4.3% and 91.7±4.8%), respectively. The 4-class classification achieved an accuracy of 62.6±7.2%, while the best 3-class classification (using left, right, and up movements) and 2-class classification (using left and right movements) achieved an accuracy of 75.6±8.4% and 87.7±8.0%, respectively. Using only a combination of the temporal and template feature groups provided further classification accuracy improvements. Presumably, this is because these feature groups utilize the movement-related cortical potentials, which are noticeably different on the left- versus right brain hemisphere for the different movements. This study shows that the cortical representation of the tongue is useful for extracting control signals for multi-class movement detection BCIs.

Electroencephalographic Recording of the Movement-Related Cortical Potential in Ecologically Valid Movements: A Scoping Review

The movement-related cortical potential (MRCP) is a brain signal that can be recorded using surface electroencephalography (EEG) and represents the cortical processes involved in movement preparation. The MRCP has been widely researched in simple, single-joint movements, however, these movements often lack ecological validity. Ecological validity refers to the generalizability of the findings to real-world situations, such as neurological rehabilitation. This scoping review aimed to synthesize the research evidence investigating the MRCP in ecologically valid movement tasks. A search of six electronic databases identified 102 studies that investigated the MRCP during multi-joint movements; 59 of these studies investigated ecologically valid movement tasks and were included in the review. The included studies investigated 15 different movement tasks that were applicable to everyday situations, but these were largely carried out in healthy populations. The synthesized findings suggest that the recording and analysis of MRCP signals is possible in ecologically valid movements, however the characteristics of the signal appear to vary across different movement tasks (i.e., those with greater complexity, increased cognitive load, or a secondary motor task) and different populations (i.e., expert performers, people with Parkinson’s Disease, and older adults). The scarcity of research in clinical populations highlights the need for further research in people with neurological and age-related conditions to progress our understanding of the MRCPs characteristics and to determine its potential as a measure of neurological recovery and intervention efficacy. MRCP-based neuromodulatory interventions applied during ecologically valid movements were only represented in one study in this review as these have been largely delivered during simple joint movements. No studies were identified that used ecologically valid movements to control BCI-driven external devices; this may reflect the technical challenges associated with accurately classifying functional movements from MRCPs. Future research investigating MRCP-based interventions should use movement tasks that are functionally relevant to everyday situations. This will facilitate the application of this knowledge into the rehabilitation setting.

Participant-specific classifier tuning increases the performance of hand movement detection from EEG in patients with amyotrophic lateral sclerosis

Objective.Brain-computer interface (BCI) systems can be employed to provide motor and communication assistance to patients suffering from neuromuscular diseases, such as amyotrophic lateral sclerosis (ALS). Movement related cortical potentials (MRCPs), which are naturally generated during movement execution, can be used to implement a BCI triggered by motor attempts. Such BCI could assist impaired motor functions of ALS patients during disease progression, and facilitate the training for the generation of reliable MRCPs. The training aspect is relevant to establish a communication channel in the late stage of the disease. Therefore, the aim of this study was to investigate the possibility of detecting MRCPs associated to movement intention in ALS patients with different levels of disease progression from slight to complete paralysis.Approach.Electroencephalography signals were recorded from nine channels in 30 ALS patients at various stages of the disease while they performed or attempted to perform hand movements timed to a visual cue. The movement detection was implemented using offline classification between movement and rest phase. Temporal and spectral features were extracted using 500 ms sliding windows with 50% overlap. The detection was tested for each individual channel and two surrogate channels by performing feature selection followed by classification using linear and non-linear support vector machine and linear discriminant analysis.Main results.The results demonstrated that the detection performance was high in all patients (accuracy 80.5 ± 5.6%) but that the classification parameters (channel, features and classifier) leading to the best performance varied greatly across patients. When the same channel and classifier were used for all patients (participant-generic analysis), the performance significantly decreased (accuracy 74 ± 8.3%).Significance.The present study demonstrates that to maximize the detection of brain waves across ALS patients at different stages of the disease, the classification pipeline should be tuned to each patient individually.

Feature Selection Combining Filter and Wrapper Methods for Motor-Imagery Based Brain-Computer Interfaces

Motor imagery (MI) based brain-computer interfaces help patients with movement disorders to regain the ability to control external devices. Common spatial pattern (CSP) is a popular algorithm for feature extraction in decoding MI tasks. However, due to noise and nonstationarity in electroencephalography (EEG), it is not optimal to combine the corresponding features obtained from the traditional CSP algorithm. In this paper, we designed a novel CSP feature selection framework that combines the filter method and the wrapper method. We first evaluated the importance of every CSP feature by the infinite latent feature selection method. Meanwhile, we calculated Wasserstein distance between feature distributions of the same feature under different tasks. Then, we redefined the importance of every CSP feature based on two indicators mentioned above, which eliminates half of CSP features to create a new CSP feature subspace according to the new importance indicator. At last, we designed the improved binary gravitational search algorithm (IBGSA) by rebuilding its transfer function and applied IBGSA on the new CSP feature subspace to find the optimal feature set. To validate the proposed method, we conducted experiments on three public BCI datasets and performed a numerical analysis of the proposed algorithm for MI classification. The accuracies were comparable to those reported in related studies and the presented model outperformed other methods in literature on the same underlying data.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain-computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

Online control of an assistive active glove by slow cortical signals in patients with amyotrophic lateral sclerosis

Objective.A brain-computer interface (BCI) allows users to control external devices using brain signals that can be recorded non-invasively via electroencephalography (EEG). Movement related cortical potentials (MRCPs) are an attractive option for BCI control since they arise naturally during movement execution and imagination, and therefore, do not require an extensive training. This study tested the feasibility of online detection of reaching and grasping using MRCPs for the application in patients suffering from amyotrophic lateral sclerosis (ALS).Approach.A BCI system was developed to trigger closing of a soft assistive glove by detecting a reaching movement. The custom-made software application included data collection, a novel method for collecting the input data for classifier training from the offline recordings based on a sliding window approach, and online control of the glove. Eight healthy subjects and two ALS patients were recruited to test the developed BCI system. They performed assessment blocks without the glove active (NG), in which the movement detection was indicated by a sound feedback, and blocks (G) in which the glove was controlled by the BCI system. The true positive rate (TPR) and the positive predictive value (PPV) were adopted as the outcome measures. Correlation analysis between forehead EEG detecting ocular artifacts and sensorimotor area EEG was conducted to confirm the validity of the results.Main results.The overall median TPR and PPV were >0.75 for online BCI control, in both healthy individuals and patients, with no significant difference across the blocks (NG versus G).Significance.The results demonstrate that cortical activity during reaching can be detected and used to control an external system with a limited amount of training data (30 trials). The developed BCI system can be used to provide grasping assistance to ALS patients.

Enhanced Performance of a Brain Switch by Simultaneous Use of EEG and NIRS Data for Asynchronous Brain-Computer Interface

Previous studies have shown the superior performance of hybrid electroencephalography (EEG)/ near-infrared spectroscopy (NIRS) brain-computer interfaces (BCIs). However, it has been veiled whether the use of a hybrid EEG/NIRS modality can provide better performance for a brain switch that can detect the onset of the intention to turn on a BCI. In this study, we developed such a hybrid EEG/NIRS brain switch and compared its performance with single modality EEG- and NIRS-based brain switch respectively, in terms of true positive rate (TPR), false positive rate (FPR), onset detection time (ODT), and information transfer rate (ITR). In an offline analysis, the performance of a hybrid EEG/NIRS brain switch was significantly improved over that of EEG- and NIRS-based brain switches in general, and in particular a significantly lower FPR was observed for the hybrid EEG/NIRS brain switch. A pseudo-online analysis was additionally performed to confirm the feasibility of implementing an online BCI system with our hybrid EEG/NIRS brain switch. The overall trend of pseudo-online analysis results generally coincided with that of the offline analysis results. No significant difference in all performance measures was also found between offline and pseudo online analysis schemes when the amount of training data was same, with one exception for the ITRs of an EEG brain switch. These offline and pseudo-online results demonstrate that a hybrid EEG/NIRS brain switch can be used to provide a better onset detection performance than that of a single neuroimaging modality.

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.