Asynchronous Brain-computer Interface Research Articles

Discriminating brainwave patterns of different control and non-control states for enhancing asynchronous brain-computer interfaces

ObjectiveIn asynchronous brain-computer interfaces (BCIs) for real-world applications, the user typically remains in non-control states most of the time and is allowed to perform various types of non-control tasks. Because of the diversity of non-control states, which include stimuli-off-based non-control (SNC), rest-based non-control (RNC), feedback-based non-control (FNC), and game-based non-control (GNC), it is difficult to discriminate between them. The objective of this study was to develop a discrimination method for different control and non-control states to enhance brain–robot cooperation. MethodsAn individualized space-frequency-based optimized complex network (ISF-OCN) was designed to discriminate the electroencephalogram (EEG) patterns of five typical states: a steady-state visual evoked potential (SSVEP)-based control (SC) state and four non-control states, i.e., SNC, RNC, FNC, and GNC. The ISF-OCN individualizes frequency bands and spatial filters to enhance the optimized complex network features for each state and uses random forest classifiers in a one-versus-rest scheme for classification.Main results: The proposed ISF-OCN outperformed four existing algorithms in both binary classification and five-state classification, regardless of data length. Notably, the ISF-OCN algorithm with a data length of 1.5 s achieved an average accuracy of 84.51 % in recognizing the five different states. ConclusionThe results demonstrate the feasibility of discriminating the four non-control states using EEG features and suggest that the proposed method is a promising new approach for discriminating control and non-control states to enhance asynchronous BCIs.

Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain–computer interface and exoskeleton

Background:Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human–computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling. New method:Based on the neurofeedback training provided by the combination of brain–computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients. Comparison with existing methods:In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system. Results:In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively. Conclusion:Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.

A Wearable Asynchronous Brain-Computer Interface Based on EEG-EOG Signals With Fewer Channels.

Brain-computer interfaces (BCIs) have tremendous application potential in communication, mechatronic control and rehabilitation. However, existing BCI systems are bulky, expensive and require laborious preparation before use. This study proposes a practical and user-friendly BCI system without compromising performance. A hybrid asynchronous BCI system was developed based on an elaborately designed wearable electroencephalography (EEG) amplifier that is compact, easy to use and offers a high signal-to-noise ratio (SNR). The wearable BCI system can detect P300 signals by processing EEG signals from three channels and operates asynchronously by integrating blink detection. The wearable EEG amplifier obtains high quality EEG signals and introduces preprocessing capabilities to BCI systems. The wearable BCI system achieves an average accuracy of 94.03±4.65%, an average information transfer rate (ITR) of 31.42±7.39 bits/min and an average false-positive rate (FPR) of 1.78%. The experimental results demonstrate the feasibility and practicality of the developed wearable EEG amplifier and BCI system. Wearable asynchronous BCI systems with fewer channels are possible, indicating that BCI applications can be transferred from the laboratory to real-world scenarios.

An Asynchronous Training-free SSVEP-BCI Detection Algorithm for Non-Equal Prior Probability Scenarios.

SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability.

Quasi-movements and attempted movements: a possible alternative to motor imagery in BCI-based neurorehabilitation

Motor imagery (MI) is a frequently used “mental trigger” for non-invasive brain-computer interfaces (BCI). Numerous studies have examined the effectiveness of MI-BCI for post-stroke rehabilitation. However, the results remain inconclusive. A potential obstacle to the effectiveness of this method could stem from an ongoing debate between the internal focus of mental activity (i.e., modeling of reality) inherent in MI and the perceived significance of sensory feedback from the actual physical environment in BCI-facilitated therapy. The requirement to allocate attentional resources to both internal actions and external consequences may contribute to the low accuracy of MI-BCI classifiers in most users. Moreover, internal focus of attention in MI may partially account for the consistent failures in combining MI-BCI with eye tracker-based interaction technologies, since external focus of attention is crucial for gaze control. A potentially effective replacement for motor imagery in BCIs is attempted movements (AMs). Studies have shown that BCIs are more successful in decoding AMs than MI (e.g., [1]). AMs involve attempted, but unrealized movements caused by paralysis or amputation. Despite their potential, AMs have received little attention, possibly because of modeling challenges with healthy participants and the widespread popularity of MI-BCIs. One approach to modeling them in healthy subjects is to use quasi-movements (QM), which are voluntary movements that are minimized by the subject to such an extent that they eventually become undetectable by objective measures [2]. However, QM has been studied even less than AM since its discovery by V.V. Nikulin and colleagues [2]. This may be due to the inadequate understanding of the difference between QM and MI. We recently proved that the sensorimotor rhythm’s event-related desynchronization in QM does not rely on the residual electromyogram (EMG), indicating that strict EMG control, which is often impossible, may not be necessary for QM contrary to prior views. As a result, QM can be embraced more frequently as an alternative to MI in BCIs [3]. Moreover, we substantiated and refined earlier findings [2] that QM possesses a striking similarity to actual movements [4]. Here, we present our initial findings on the asynchronous classification of QM. These findings may serve as a foundation for a real-time QM-BCI system. We used the EEG data recorded from 23 participants who synchronized their QM and MI with rhythmic sound triplets. A convolutional neural network called SimpleNet [5], with high interpretability, was trained on a subset of individual data separately for QM and IM, compared to a referential non-motor task. The network was applied offline and was unaware of sound timing, to another subset in 1.5-second windows with 0.1-second steps. QM/IM were detected when four consecutive positive windows occurred with a refractory period of three seconds. Due to the high variability in MI-BCI performance among untrained individuals, we only assessed classifier performance amongst participants exhibiting a TPR (true positive rate) greater than 0.5. We identified 7 such participants in QM and 5 in MI. QM showed better intention detection than MI, though not significantly, according to the Mann-Whitney test. The mean values ± standard deviation for TPR were 0.81±0.12 in QM and 0.77±0.12 in MI, while the false alarm rate (s–1) was 0.03±0.02 in QM and 0.04±0.03 in MI, and the response time (s) was 2.81±0.06 in QM and 2.86±0.10 in MI. Our initial findings on asynchronous BCI modeling are consistent with previous studies that have demonstrated superior QM classification in synchronous paradigms as compared to MI [2]. Notably, only minor hyperparameter optimization was performed for SimpleNet, leaving ample room for improvement in classification. Given the superior classification of AM over MI [1] and the promising preliminary findings presented here, the use of AM by end-users appears to be a viable option. Utilizing QM in studies that model AM could likely lead to further promotion and development of this technique. Additionally, the comparable nature of QM, AM, and overt movement implies their applicability in conveying intention via gaze-controlled interfaces. Although overt motor confirmation is suitable, MI-BCIs have shown inadequacies in this regard.

The Effects of Different Brain Regions on fNIRS-based Task-state Detection in Speech Imagery.

Brain-computer interface (BCI) based on speech imagery can decode users' verbal intent and help people with motor disabilities communicate naturally. Functional near-infrared spectroscopy (fNIRS) is a commonly used brain signal acquisition method. Asynchronous BCI can response to control commands at any time, which provides great convenience for users. Task state detection, defined as identifying whether user starts or continues covertly articulating, plays an important role in speech imagery BCIs. To better distinguish task state from idle state during speech imagery, this work used fNIRS signals from different brain regions to study the effects of different brain regions on task state detection accuracy. The imagined tonal syllables included four lexical tones and four vowels in Mandarin Chinese. The brain regions that were measured included Broca's area, Wernicke's area, Superior temporal cortex and Motor cortex. Task state detection accuracies of imagining tonal monosyllables with four different tones were analyzed. The average accuracy of four speech imagery tasks based on the whole brain was 0.67 and it was close to 0.69, which was the average accuracy based on Broca's area. The accuracies of Broca's area and the whole brain were significantly higher than those of other brain regions. The findings of this work demonstrated that using a few channels of Broca's area could result in a similar task state detection accuracy to that using all the channels of the brain. Moreover, it was discovered that speech imagery with tone 2/3 tasks yielded higher task state detection accuracy than speech imagery with other tones.

Asynchronous Motor Imagery BCI and LiDAR-Based Shared Control System for Intuitive Wheelchair Navigation

Mapping drivers’ thoughts directly to mobility system control would make driving more intuitive as if the mobility system is an extension of their own body. Such a system would allow patients with motor disabilities to drive, as it would not require any physical movement. In this paper, we therefore propose a brain-controlled mobility system that analyzes real-time neural signals elicited from motor imagery, an imagination of different body movements. As such asynchronous brain-computer interfaces (BCIs) are prone to error, our system contains shared control capabilities that take into consideration continuously updated information of the surrounding environment along with electroencephalogram (EEG) signals to improve navigating performance without precise and accurate control from the driver. With our shared control method that uses a wheelchair with light detection and ranging (LiDAR) and inertial measurement unit (IMU) sensors, we held a comparative study in which participants drove our wheelchair with and without our shared control approach using either our brain-controlled system or a keyboard in a physical environment. The experimental results show that among the five participants, the three participants that failed the driving task with the asynchronous BCI-based system could also successfully complete it using our shared control approach. Furthermore, our approach narrows the gap between driving with neural signals and driving with a widely used interface in terms of both elapsed time and safety. These results show not only the potential of brain signals for driving, but also the applicability of BCIs to real-life situations.

CVT-Based Asynchronous BCI for Brain-Controlled Robot Navigation.

Brain-computer interface (BCI) is a typical direction of integration of human intelligence and robot intelligence. Shared control is an essential form of combining human and robot agents in a common task, but still faces a lack of freedom for the human agent. This paper proposes a Centroidal Voronoi Tessellation (CVT)-based road segmentation approach for brain-controlled robot navigation by means of asynchronous BCI. An electromyogram-based asynchronous mechanism is introduced into the BCI system for self-paced control. A novel CVT-based road segmentation method is provided to generate optional navigation goals in the road area for arbitrary goal selection. An event-related potential of the BCI is designed for target selection to communicate with the robot. The robot has an autonomous navigation function to reach the human selected goals. A comparison experiment in the single-step control pattern is executed to verify the effectiveness of the CVT-based asynchronous (CVT-A) BCI system. Eight subjects participated in the experiment, and they were instructed to control the robot to navigate toward a destination with obstacle avoidance tasks. The results show that the CVT-A BCI system can shorten the task duration, decrease the command times, and optimize navigation path, compared with the single-step pattern. Moreover, this shared control mechanism of the CVT-A BCI system contributes to the promotion of human and robot agent integration control in unstructured environments.

A Hybrid Method Fusing Frequency Recognition With Attention Detection to Enhance an Asynchronous Brain-Computer Interface.

One critical problem in controlling an asynchronous brain-computer interface (BCI) system is to discriminate between control and idle states. This paper proposes a hybrid attention detection and frequency recognition method based on weighted Dempster-Shafer theory (ADFR-DS), which integrates information of different aspects of the task from two brain regions, to enhance asynchronous control performance of a steady-state visual evoked potential (SSVEP)-based BCI system. The ADFR-DS method utilizes a hybrid architecture to process electroencephalogram (EEG) data from different channels simultaneously: an individualized frequency band based optimized complex network (IFBOCN) algorithm processes neural activity from the prefrontal area for attention detection, and an ensemble task-related component analysis (eTRCA) algorithm processes data from the occipital area for frequency recognition. The ADFR-DS method then fuses their classification results at decision level to generate the final output of the BCI system. A novel weighted Dempster-Shafer fusion method was proposed to enhance the fusion performance. This study evaluated the proposed method using a 40-target dataset recorded from 35 participants. The proposed method outperformed the eTRCA algorithm in the true positive rate (TPR), true negative rate (TNR), accuracy (ACC) and information transfer rate (ITR). Specifically, ADFR-DS improved the average ACC of eTRCA from 62.71% to 69.30%, and improved the average ITR from 184.28 bits/min to 216.89 bits/min (data length 0.3 s). The results suggest that the proposed ADFR-DS method can enhance asynchronous SSVEP-based BCI systems.

BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.

The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three - probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.

A Hybrid Asynchronous Brain–Computer Interface Based on SSVEP and Eye-Tracking for Threatening Pedestrian Identification in Driving

A brain–computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has achieved remarkable performance in the field of automatic driving. Prolonged SSVEP stimuli can cause driver fatigue and reduce the efficiency of interaction. In this paper, a multi-modal hybrid asynchronous BCI system combining eye-tracking and EEG signals is proposed for dynamic threatening pedestrian identification in driving. Stimuli arrows of different frequencies and directions are randomly superimposed on pedestrian targets. Subjects scan the stimuli according to the direction of arrows until the threatening pedestrian is selected. The thresholds determined by offline experiments are used to distinguish between working and idle states of the asynchronous online experiments. Subjects need to judge and select potentially threatening pedestrians in online experiments according to their own subjective experience. The three proposed decisions filter out the results with low confidence and effectively improve the selection accuracy of hybrid BCI. The experimental results of six subjects show that the proposed hybrid asynchronous BCI system achieves better performance compared with a single SSVEP-BCI, with an average selection time of 1.33 s, an average selection accuracy of 95.83%, and an average information transfer rate (ITR) of 67.50 bits/min. These results indicate that our hybrid asynchronous BCI has great application potential in dynamic threatening pedestrian identification in driving.

뇌-컴퓨터 인터랙션 오탐 저감을 위한 뇌파-턱 근전도 통합 신호 제어 프로토콜 개발

Objective: The purpose of this study was to investigate whether electromyogram (EMG) induced by jaw-clenching can be accurately observed in steady-state visually evoked potential (SSVEP) signals at occipital lobes as well as temporal lobes. We also investigated a feasibility that the fused SSVEP data with the jaw-clenching EMG can be classified as a function of task types characterized by SSVEPs with and without the short and long clenching noise. Based on this hypothesis, we proposed a novel command protocol to effectively and efficiently reduce false alarms caused by using SSVEP brain-computer interface (BCI).<BR><BR>Background: Much attention has been paid to various brain-computer interfaces for neural rehabilitation and alternative communication channels for disabled people with severe neurological disorders. However, despite the interests in BCI, false alarms caused by interference effects between command flickers with different frequencies or internal and external noises have been still problematic and have yet to be investigated. Even though protocols to correct the false alarms based on error-related potential (ErrP) or eye blinks have been proposed, it has still limitation due to errors inherent in involuntary reactions of humans. Thus, it is necessary to develop a protocol to address the issue and propose a method insusceptible to the involuntary reactions.<BR><BR>Method: Nine undergraduate students (3 female) voluntarily participated in the experiment. They were asked to divide their attention between the two squares randomly oscillating with 8.57 and 10Hz and perform three types of tasks characterized by SSVEP only, short clenching, and long clenching conditions. They carried out the three tasks with a semi-counterbalanced and random order and rest interval between the tasks was set to 10 minutes to minimize order and carryover effects. It took about 12 minutes in completing the experimental tasks for each participant. We used five machine learning algorithms of binary classification to compare the performance in classifying the brainwave signals characterized by the three tasks.<BR><BR>Results: As a result of the classification comparison, random forest showed the highest classification performance of more than 0.85 (AUC) as follows: 0.88 (short clenching vs. SSVEP), 0.87 (long clenching vs. SSVEP), 0.85 (long clenching vs. short clenching condition) in the test data set. Notably, the findings implicated a clear classification in the long cand short clenching condition even at the frequency band of more than 20Hz. For the participants whose highest weight factor was shown at the occipital lobe, it can be possible to reduce false alarms in the SSVEP BCI with only single electrode at the occipital lobe.<BR><BR>Conclusion: The findings found in this study implicate that the proposed protocol can be used to effectively and efficiently correct false alarms in event-related potential (ERP), event-related desynchronization/synchronization (ERD/ERS) BCI as well as SSVEP BCI. On top of that, classifying the subtle changes in the jaw clenching types enables BCI developers to create additional commands to modulate their BCI system. The results can be greatly improved if weight factors in the binary classification are modulated as a function of individual characteristics.<BR><BR>Application: The findings obtained in this study can be utilized in developing asynchronous BCI very robust to false alarms and expected to provide a future direction into hybrid BCI system.

Associative cued asynchronous BCI induces cortical plasticity in stroke patients.

ObjectiveWe propose a novel cue‐based asynchronous brain–computer interface(BCI) for neuromodulation via the pairing of endogenous motor cortical activity with the activation of somatosensory pathways.MethodsThe proposed BCI detects the intention to move from single‐trial EEG signals in real time, but, contrary to classic asynchronous‐BCI systems, the detection occurs only during time intervals when the patient is cued to move. This cue‐based asynchronous‐BCI was compared with two traditional BCI modes (asynchronous‐BCI and offline synchronous‐BCI) and a control intervention in chronic stroke patients. The patients performed ankle dorsiflexion movements of the paretic limb in each intervention while their brain signals were recorded. BCI interventions decoded the movement attempt and activated afferent pathways via electrical stimulation. Corticomotor excitability was assessed using motor‐evoked potentials in the tibialis‐anterior muscle induced by transcranial magnetic stimulation before, immediately after, and 30 min after the intervention.ResultsThe proposed cue‐based asynchronous‐BCI had significantly fewer false positives/min and false positives/true positives (%) as compared to the previously developed asynchronous‐BCI. Linear‐mixed‐models showed that motor‐evoked potential amplitudes increased following all BCI modes immediately after the intervention compared to the control condition (p <0.05). The proposed cue‐based asynchronous‐BCI resulted in the largest relative increase in peak‐to‐peak motor‐evoked potential amplitudes(141% ± 33%) among all interventions and sustained it for 30 min(111% ± 33%).InterpretationThese findings prove the high performance of a newly proposed cue‐based asynchronous‐BCI intervention. In this paradigm, individuals receive precise instructions (cue) to promote engagement, while the timing of brain activity is accurately detected to establish a precise association with the delivery of sensory input for plasticity induction.

An adaptive closed-loop ECoG decoder for long-term and stable bimanual control of an exoskeleton by a tetraplegic

Objective. The article aims at addressing 2 challenges to step motor brain-computer interface (BCI) out of laboratories: asynchronous control of complex bimanual effectors with large numbers of degrees of freedom, using chronic and safe recorders, and the decoding performance stability over time without frequent decoder recalibration. Approach. Closed-loop adaptive/incremental decoder training is one strategy to create a model stable over time. Adaptive decoders update their parameters with new incoming data, optimizing the model parameters in real time. It allows cross-session training with multiple recording conditions during closed loop BCI experiments. In the article, an adaptive tensor-based recursive exponentially weighted Markov-switching multi-linear model (REW-MSLM) decoder is proposed. REW-MSLM uses a mixture of expert (ME) architecture, mixing or switching independent decoders (experts) according to the probability estimated by a ‘gating’ model. A Hidden Markov model approach is employed as gating model to improve the decoding robustness and to provide strong idle state support. The ME architecture fits the multi-limb paradigm associating an expert to a particular limb or action. Main results. Asynchronous control of an exoskeleton by a tetraplegic patient using a chronically implanted epidural electrocorticography (EpiCoG) recorder is reported. The stable over a period of six months (without decoder recalibration) eight-dimensional alternative bimanual control of the exoskeleton and its virtual avatar is demonstrated. Significance. Based on the long-term (>36 months) chronic bilateral EpiCoG recordings in a tetraplegic (ClinicalTrials.gov, NCT02550522), we addressed the poorly explored field of asynchronous bimanual BCI. The new decoder was designed to meet to several challenges: the high-dimensional control of a complex effector in experiments closer to real-world behavior (point-to-point pursuit versus conventional center-out tasks), with the ability of the BCI system to act as a stand-alone device switching between idle and control states, and a stable performance over a long period of time without decoder recalibration.

A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface

BackgroundAn asynchronous event-related potential-based brain computer interface (ERP-BCI) permits the subjects to output intentions at their own pace, which provides a more free and practical communication pathways without the need for muscle activity. The core of constructing this type of system is to discriminate both the intentions and brain states. New methodsThis study proposes a fisher linear discriminant analysis classification algorithm fused with naïve Bayes (B-FLDA) for the ERP-BCI to simultaneous recognize the subjects’ intentions, working and idle states. This method uses the spectral characteristics of visual-evoked potential and the time-domain characteristics of ERP to simultaneously detect brain states and target stimulus, and obtain the final discrimination result through probability fusion. ResultsThe accuracy and the information transfer rate increase to 98.61% and 62.80 bits/min under 10 repetitions and 1 repetition, respectively. The three parameters of receiver operator characteristic curve have achieved better performance. Comparison with existing methodsTen subjects participate in this study with the proposed algorithms and two other control methods. The accuracy and information transfer rate of this algorithm are better than the other methods. ConclusionsIt indicates that the naïve Bayes-FLDA algorithm is able to improve the performance of an asynchronous BCI system by detecting the intentions and states simultaneously.

A high-performance brain switch based on code-modulated visual evoked potentials

Objective. Asynchronous brain-computer interfaces (BCIs) are more practical and natural compared to synchronous BCIs. A brain switch is a standard asynchronous BCI, which can automatically detect the specified change of the brain and discriminate between the control state and the idle state. The current brain switches still face challenges on relatively long reaction time (RT) and high false positive rate (FPR). Approach. In this paper, an online electroencephalography-based brain switch is designed to realize a fast reaction and keep long idle time (IDLE) without false positives (FPs) using code-modulated visual evoked potentials (c-VEPs). Two stimulation paradigms were designed and compared in the experiments: multi-code concatenate modulation (concatenation mode) and single-code periodic modulation (periodic mode). Using a task-related component analysis-based detection algorithm, EEG data can be decoded into a series of code indices. Brain states can be detected by a template matching approach with a sliding window on the output series. Main results. The online experiments achieved an average RT of 1.49 s when the average IDLE for each FP was 68.57 min (1.46 × 10−2 FP min−1) or an average RT of 1.67 s without FPs. Significance. This study provides a practical c-VEP based brain switch system with both fast reaction and low FPR during idle state, which can be used in various BCI applications.

Time Series Shapelet-Based Movement Intention Detection Toward Asynchronous BCI for Stroke Rehabilitation

Brain computer interface (BCI) systems for neurorehabilitation have received increasing attention over the past decade. These systems provide an alternative approach to restore lost motor functions in stroke patients by inducing brain plasticity. To utilize BCI systems for stroke rehabilitation, user movement intentions from electroencephalogram are used as triggers for rehabilitation tools such as electrical stimulators. A reliable movement intention detection plays a vital role in an effective rehabilitation to establish a relation between movement intentions and corresponding feedback from a rehabilitation tool. We therefore propose a novel movement intention detection algorithm based on time series shapelets. We collected a dataset of subjects performing a self-pace ankle dorsiflexion and tested the algorithm in both classification and pseudo-online detection. The classification results demonstrate that our proposed algorithm significantly outperforms six other algorithms and achieves the highest classification performance with F1-score of 0.82. For pseudo-online detection, our algorithm gains very high performance in all performance metrics with 69% True Positive Rate (TPR), 8 False Positives per minute (FPs/min), and 44 ms Detection Latency (DL). Our proposed algorithm does not only provide competitive performance to state-of-the-art algorithms in terms of TPR, but also maintain low FPs/min and DL. In addition, the DL of our proposed algorithm is low enough to induce effective brain plasticity for neurorehabilitation. These promising results enlighten the development of asynchronous BCI systems based on time series mining techniques to enhance stroke rehabilitation results.

System Architecture for Brain-Computer Interface based on Machine Learning and Internet of Things

Brain functions are required to be read for curing neurological illness. Brain-Computer Interface (BCI) connects the brain to the digital world for brain signals receiving, recording, processing, and comprehending. With a Brain-Computer Interface (BCI), the information from the user’s brain is fed into actuation devices, which then carry out the actions programmed into them. The Internet of Things (IoT) has made it possible to connect a wide range of everyday devices. Asynchronous BCIs can benefit from an improved system architecture proposed in this paper. Individuals with severe motor impairments will particularly get benefit from this feature. Control commands were translated using a rule-based translation algorithm in traditional BCI systems, which relied only on EEG recordings of brain signals. Examining BCI technology’s various and cross-disciplinary applications, this argument produces speculative conclusions about how BCI instruments combined with machine learning algorithms could affect the forthcoming procedures and practices. Compressive sensing and neural networks are used to compress and reconstruct ECoG data presented in this article. The neural networks are used to combine the classifier outputs adaptively based on the feedback. A stochastic gradient descent solver is employed to generate a multi-layer perceptron regressor. An example network is shown to take a 50% compression ratio and 89% reconstruction accuracy after training with real-world, medium-sized datasets as shown in this paper.

Idle-state detection in motor imagery of articulation using early information: A functional Near-infrared spectroscopy study

A speech imagery-based brain computer interface (BCI) provides an alternative way for people to interact with the outside world intuitively. Most speech imagery BCIs based on functional near-infrared spectroscopy (fNIRS) are disadvantageous in applications outside the laboratory due to being incapable of detecting asynchronous (self-paced) actions. This work aimed to develop a two-class asynchronous BCI that detects idle state in the context of motor imagery of articulation (MIA). In this study, 19 healthy subjects were asked to rehearse the Chinese vowels/a/and/u/covertly and to accommodate a rest state. A feature selection strategy was designed to combine time domain, spatial domain, and functional connectivity features. All differentiating information was extracted from fNIRS signals in a 0–2.5 s time window. Among single-modality features, the centrality features of brain networks performed better than any other features and yielded a subject-dependent classification accuracy rate of 75.1%. The subject-dependent classification accuracy rate of the combined features at 78.9% was significantly better than that of any single feature. These results demonstrate that it is feasible to detect idle states from active MIA states in a reduced time window size. The proposed combination of information propagation pattern, spatial pattern, and activation pattern was meaningful for extracting discriminative information, hence improving classification accuracy. The high classification accuracy and fast information transfer rate of the presented BCI show promising practicality for real world applications.

Spatio-temporal equalization multi-window algorithm for asynchronous SSVEP-based BCI

Objective. Asynchronous brain-computer interfaces (BCIs) show significant advantages in many practical application scenarios. Compared with the rapid development of synchronous BCIs technology, the progress of asynchronous BCI research, in terms of containing multiple targets and training-free detection, is still relatively slow. In order to improve the practicability of the BCI, a spatio-temporal equalization multi-window algorithm (STE-MW) was proposed for asynchronous detection of steady-state visual evoked potential (SSVEP) without the need for acquiring calibration data. Approach. The algorithm used SIE strategy to intercept EEG signals of different lengths through multiple stacked time windows and statistical decisions-making based on Bayesian risk decision-making. Different from the traditional asynchronous algorithms based on the ‘non-control state detection’ methods, this algorithm was based on the ‘statistical inspection-rejection decision’ mode and did not require a separate classification of non-control states, so it can be effectively applied to detections for large-scale candidates. Main results. Online experimental results involving 14 healthy subjects showed that, in the continuously input experiments of 40 targets, the algorithm achieved the average recognition accuracy of and the average information transfer rate (ITR) of . At the same time, the average false alarm rate in the 240 s resting state test was . In the free spelling experiments involving patients with severe amyotrophic lateral sclerosis, the subjects achieved an accuracy of 92.7% and an average ITR of 43.65 bits min−1 in two free spelling experiments. Significance. This algorithm can achieve high-performance, high-precision, and asynchronous detection of SSVEP signals with low algorithm complexity and false alarm rate under multi-targets and training-free conditions, which is helpful for the development of asynchronous BCI systems.