Towards Development of a 3-State Self-Paced Brain-Computer Interface

This study employs compressive sensing (CS) for accelerating the overall data processing in a brain-computer interface (BCI). CS is a joint signal acquisition and compression scheme. By projecting EEG data streams onto a lower dimensional basis that is incoherent with their inherent structure, CS compresses the data and preserves their salient information. This paper presents a novel self-paced BCI (SBCI) that extracts compact and relevant features from EEG using CS. To detect an intentional control (IC) state from user's EEG, the signal structure of a specific voluntary movement that is common in all trials is identified. This compressed subset of basis functions is constructed during the BCIs training mode and is then used to acquire features during its testing mode. Experimental results show that our proposed method can efficiently detect voluntary movements for a SBCI, with potential speed increases up to 12 times from the state-of-art prototype.

The low-frequency asynchronous switch design (LF-ASD) was introduced as a direct brain computer interface (BCI) technology for asynchronous control applications. The LF-ASD has the advantage that it is operational at any time and not only at specific defined periods. It is activated only when a user intends control, otherwise it maintains an inactive state output. The intended control results from a certain movement attempt such as finger flexion. This paper presents the evaluation of a modified LF-ASD design with data collected from individuals with high-level spinal cord injuries and able-bodied subjects. The modifications are related to incorporating into the system more knowledge about the movement attempt. Specifically, the past values of the features extracted from the EEG signal related to the movement attempts are used. The error characteristics of this new asynchronous brain switch design are significantly better than the previous LF-ASD design, with true positive rate increases of approximately 8.5% for false positive rates in the range of 1-2%

The low-frequency asynchronous switch design (LF-ASD) has been introduced as a direct brain interface (BI) for asynchronous control applications. Asynchronous interfaces, as opposed to synchronous interfaces, have the advantage of being operational at all times and not only at specific system-defined periods. This paper modifies the LF-ASD design by incorporating into the system more knowledge about the attempted movements. Specifically, the history of feature values extracted from the EEG signal is used to detect a right index finger movement attempt. Using data collected from individuals with high-level spinal cord injuries and able-bodied subjects, it is shown that the error characteristics of the modified design are significantly better than the previous LF-ASD design. The true positive rate percentage increased by up to 15 which corresponds to 50% improvement when the system is operating with false positive rates in the 1–2% range.

During recent years there has been a growing interest in Brain Computer Interface (BCI) systems as an alternative means of interaction with the external world for people with severe motor disabilities. The use of the P300 event-related potentials as control feature allows users to choose between various options (letters or icons) requiring a very short calibration phase. The aim of this work is to improve performances and flexibility of P300 based BCIs. An efficient BCI system should be able to understand user's intentions from the ongoing EEG, abstaining from doing a selection when the user is engaged in a different activity, and changing its speed of selection depending on current user's attention level. Our self-paced system addresses all these issues representing an important step beyond the classical synchronous P300 BCI that forces the user in a continuous control task. Experimentation has been performed on 10 healthy volunteers acting on a BCI-controlled domestic environment in order to demonstrate the potential usability of BCI systems in everyday life. Results show that the self-paced BCI increases information transfer rate with respect to the synchronous one, being very robust, at the same time, in avoiding false negatives when the user is not engaged in a control task.

Our research has focused on developing a brain-controlled switch that is suitable for asynchronous control applications. We have developed a switch, the Low Frequency Asynchronous Switch Design (LF-ASD) that users can activate by imagining movement. On-line implementations of the LF-ASD has shown promising results in respond to actual index finger flexions and imagined finger flexions within able-bodied subjects. This work reports the results of our first test with subjects with high-level spinal-cord injuries. In this study, two subjects with high-level spinal-cord injuries were able to control the LF-ASD with imagined voluntary movements with hit (true positive) rates from 45-48% and false positive rates below 1%.

In this study we compared tactile and visual feedbacks for the motor imagery-based brain–computer interface (BCI) in five healthy subjects. A vertical green bar from the center of the fixing cross to the edge of the screen was used as visual feedback. Vibration motors that were placed on the forearms of the right and the left hands and on the back of the subject’s neck were used as tactile feedback. A vibration signal was used to confirm the correct classification of the EEG patterns of the motor imagery of right and left hand movements and the rest task. The accuracy of recognition in the classification of the three states (right hand movement, left hand movement, and rest) in the BCI without feedback exceeded the random level (33% for the three states) for all the subjects and was rather high (67.8% ± 13.4% (mean ± standard deviation)). Including the visual and tactile feedback in the BCI did not significantly change the mean accuracy of recognition of mental states for all the subjects (70.5% ± 14.8% for the visual feedback and 65.9% ± 12.4% for the tactile feedback). The analysis of the dynamics of the movement imagery skill in BCI users with the tactile and visual feedback showed no significant differences between these types of feedback. Thus, it has been found that the tactile feedback can be used in the motor imagery-based BCI instead of the commonly used visual feedback, which greatly expands the possibilities of the practical application of the BCI.

In this work, an electroencephalographic analysis-based, self-paced (asynchronous) brain-computer interface (BCI) is proposed to control a mobile robot using four different navigation commands: turn right, turn left, move forward and move back. In order to reduce the probability of misclassification, the BCI is to be controlled with only two mental tasks (relaxed state versus imagination of right hand movements), using an audio-cued interface. Four healthy subjects participated in the experiment. After two sessions controlling a simulated robot in a virtual environment (which allowed the user to become familiar with the interface), three subjects successfully moved the robot in a real environment. The obtained results show that the proposed interface enables control over the robot, even for subjects with low BCI performance.

Phase-amplitude coupling (PAC), the modulation of high-frequency neural oscillations by the phase of slower oscillations, is increasingly recognized as a marker of goal-directed motor behavior. Despite this interest, its specific role and potential value in decoding attempted motor movements remain unclear. This study investigates whether PAC-derived features can be leveraged to classify different motor behaviors from ECoG signals within Brain-Computer Interface (BCI) systems. ECoG data were collected using the WIMAGINE implant during BCI experiments with a tetraplegic patient performing mental motor tasks. The data underwent preprocessing to extract complex neural oscillation features (amplitude, phase) through spectral decomposition techniques. These features were then used to quantify PAC by calculating different coupling indices. PAC metrics served as input features in a machine learning pipeline to evaluate their effectiveness in predicting mental tasks (idle state, right-hand movement, left-hand movement) in both offline and pseudo-online modes. The PAC features demonstrated high accuracy in distinguishing among motor tasks, with key classification features highlighting the coupling of theta/low-gamma and beta/high-gamma frequency bands. These preliminary findings hold significant potential for advancing our understanding of motor behavior and for developing optimized BCI systems.

Brain-Computer Interface (BCI) can provide users with an alternative/augmentative interaction path, based on the interpretation of their brain activity. Steady State Visual Evoked Potentials (SSVEP) paradigm has many appealing features, aiming at implementing BCI-enabled communication-control applications. In this paper, we present a complete signal processing chain for a self-paced, SSVEP-based BCI. The proposed approach mostly focuses at reducing the user effort in dealing with BCI, featuring no need of user-specific calibration or training. In this paper, the classification algorithm is introduced and first validated on offline waveforms, aiming at improving classification accuracy and minimizing the false positive rate. Then, implementation of an online, self-paced SSVEP BCI is illustrated. The scheme refers to a four-way choice and exploits discrimination between intentional control states and nocontrol ones. Good performance is achieved, both in terms of true positive rate (>94%), as well as low false positive rate (0.26 min(-1)), even in experiments carried out outside lab-controlled conditions.

For developing brain computer interface (BCI) applications, electroencephalography (EEG) is the most widely used measurement method due to its noninvasiveness, high temporal resolution, and portability. EEG signal contains sufficient neural information about each human task, which makes the extracting, and decoding of each task-related information is still challenging, especially to improve the existing BCI performances. In this paper, we present a comparison analysis to find the most relevant features and the most suitable classification method for decoding motor imagery for EEG-based BCI. Therefore, some signal processing and machine learning techniques have applied for features extraction and classification phases. For the decomposition of EEG signal, we used three type of features [EEG signal mean, root mean square (RMS) and Relative of band power (RBP)]. In addition, we investigated an analytical comparison between three methods of classification [Support Vector Machine (SVM), Linear Discriminant Analysis and K-Nearest Neighbors]. The methods were validated using a publicly available dataset (BCI Competition IV-III-a) to discriminate between two mental states (right and left hand movements) using 10-fold cross-validation. SVM method gave better classification accuracy of 76.4% using relative band powers as potential EEG features.

Brain–computer interface (BCI) technology provides a communication pathway using the human brain motor imaginary signal to develop applications like robotic hands and automated wheelchair, which are useful for the people who come with several motor disabilities. However, the signals which are acquired in a non-invasive approach come with various types of artifacts which badly effect on the accuracy of the prediction. For the above purposes, this paper proposes a model of human motor activity recognition using electroencephalogram (EEG) signal, in which three major time–frequency domain de-noising algorithms, i.e., Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Savitzky–Golay filter are adopted for de-noising the signals acquired in a non-invasive approach. In this paper, initially, the EEG signals are de-noised using de-noising algorithm. After that, important statistical features from selected EEG channels of the dataset are extracted. Finally, the Support Vector Machine (SVM) classification algorithm is used for classifying particular motor activities. Those three major time–frequency domain de-noising algorithms are compared based on five comparison metrics, i.e., mean squared error (MSE), mean absolute error (MAE), signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), cross-correlation and the classification accuracy using SVM classification. For evaluating the proposed model, an online benchmarked dataset with four classes of motor activities has been used. Among the four classes, the first two classes are used in this research. Those are ‘Left-hand movement’ as Class 1 and ‘Right-hand movement’ as Class 2. The experimental result, the DWT-based de-noising method, shows optimal performance.

Neural Correlates of User-initiated Motor Success and Failure – A Brain–Computer Interface Perspective

A brain-computer interface (BCI) allows the user to control a device or software with brain activity. Many BCIs rely on dynamics of brain oscillations related to semantic and motor imaginary tasks in the electroencephalogram (EEG) and magnetoencephalography (MEG). Decoding brain activity of corresponding high-level tasks may lead to an independent and intuitively controlled BCI. Most of today’s BCI research focuses on analyzing EEG and MEG which provide only limited signal-to-noise ratio. Derived electrocorticographic (ECoG) signals allow the investigation of spatially highly focused task-related activation within the high-gamma frequency band, making the discrimination of individual finger movements or complex grasping tasks possible. Common spatial patterns (CSPs) are commonly used for BCI systems and provide a powerful tool for feature optimization and dimensionality reduction. This work focused on the discrimination of (i) three complex hand movements as well as (ii) hand movement and idle state. Two subjects S1 and S2 performed single “open,” “peace,” and “fist” hand poses in multiple trials and (iii) one subject S3 controlled a humanoid in the remote place. Signals in the high-gamma frequency range between 100 and 500 Hz were spatially filtered based on a CSP algorithm for (i), (ii), and (iii). Additionally, a manual feature selection approach was tested for (i). A multi-class linear discriminant analysis (LDA) showed for (i) an error rate of 13.89 %/7.22 % and 18.42 %/1.17 % for S1 and S2 using manually/CSP selected features, where for (ii) a two-class LDA leads to a classification error of 13.39 % and 2.33 % for S1 and S2, respectively. For motor imaginary to remotely control the humanoid, (iii) S3 was able to perform such control over four sessions: three of those were performed consecutively over 1 day and the last one was performed 2 days afterward. After each session, the performance of the classifier improved, reaching about 90 % in the end. This experiment showed that ECoG-based motor imagery performed well despite a short training period, providing future possibility for MEG-based BCI with higher sensitivity than present MEG sensors.

Brain Computer Interface (BCI) is a system that directly utilize Electroencephalograph (EEG) signals to control external devices without aid from any limb of the body. BCI system consists of brainwave acquisition, signal processing, feature extraction and classification. A design of BCI system has been developed by using a wireless EEG Emotiv EPOC neuroheadset and OpenViBE. Both of them are open-source system which gives opportunity to develop our BCI system freely. Mu wave is extracted from the acquired brainwaves when the subject imagined hand movement. Mu wave can be obtained on FC5 and FC6, where premotor activities take place, by apply it to a 8 – 13 Hz bandpass filter. Mu wave power which is the square of EEG signal amplitude is extracted to be classified into two different classes. Feature classification is done by using Support Vector Machine (SVM) in offline classification and online training. EEG signal was acquired on three healthy subjects without well training with BCI control. The task of subjects are imaginary movement of right and left hand with stimulation by a left and right arrow on the screen. Configuration for training and testing phase has been successfully done in OpenViBE towards online application. The mean recognition rate in offline testing and single trial classification is 60.63% for right arrow and 45.93% for left arrow on all subjects.

An EEG-based brain-computer interface (BCI) is a direct connection between the human brain and the computer. Such a communication system is needed by patients with severe motor impairments (e.g. late stage of Amyotrophic Lateral Sclerosis) and has to operate in real-time. This paper describes the selection of the appropriate components to construct such a BCI and focuses also on the selection of a suitable programming language and operating system. The multichannel system runs under Windows 95, equipped with a real-time Kernel expansion to obtain reasonable real-time operations on a standard PC. Matlab controls the data acquisition and the presentation of the experimental paradigm, while Simulink is used to calculate the recursive least square (RLS) algorithm that describes the current state of the EEG in real-time. First results of the new low-cost BCI show that the accuracy of differentiating imagination of left and right hand movement is around 95%.