An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

The lived experiences of play and the perspectives of disabled children and their parents surrounding brain-computer interfaces

Brain-computer interfaces (BCI) offer promise to the play of children with significant physical impairments, as BCI technology can enable disabled children to control computer devices, toys, and robots using only their brain signals. However, there is little research on the unique needs of disabled children when it comes to BCI-enabled play. Thus, this paper explored the lived experiences of play for children with significant physical impairments and examined how BCI could potentially be implemented into disabled children's play experiences by applying a social model of childhood disability. Descriptive qualitative methodology was employed by conducting four semi-structured interviews with two children with significant physical impairments and their parents. We found that disabled children's play can be interpreted as passive or active depending on one's definition and perceptions surrounding play. Moreover, disabled children continue to face physical, economic, and technological barriers in their play, as well as play restrictions from physical impairments. We urge that future research should strive to directly hear from disabled children themselves, as their perspectives may differ from their parents' views. Also, future BCI development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.Implications for RehabilitationAssistive technology research should strive to examine the social, infrastructural, and environmental barriers that continue to disable and restrict participation for disabled children and their families through applying a social model of childhood disability and other holistic frameworks that look beyond individual factorsFuture research that examines the needs and lives of disabled children should strive to directly seek the opinions and perspectives of disabled children themselvesBrain-computer interface development should strive to incorporate video games, recreational and entertainment applications/platforms, toys and switch-adapted toys, and power wheelchairs to better support the play of children with significant physical impairments.

Relationship of SSVEP response between flash frequency conditions

This study delves into the application of Brain-Computer Interfaces (BCIs), focusing on exploiting Steady-State Visual Evoked Potentials (SSVEPs) as communication tools for individuals facing mobility impairments. SSVEP-BCI systems can swiftly transmit substantial volumes of information, rendering them suitable for diverse applications. However, the efficacy of SSVEP responses can be influenced by variables such as the frequency and color of visual stimuli. Through experiments involving participants equipped with electrodes on the brain's visual cortex, visual stimuli were administered at 4, 17, 25, and 40Hz, using white, red, yellow, green, and blue light sources. The results reveal that white and green stimuli evoke higher SSVEP responses at lower frequencies, with color's impact diminishing at higher frequencies. At low light intensity (1W), white and green stimuli elicit significantly higher SSVEP responses, while at high intensity (3W), responses across colors tend to equalize. Notably, due to seizure risks, red and blue lights should be used cautiously, with white and green lights preferred for SSVEP-BCI systems. This underscores the critical consideration of color and frequency in the design of effective and safe SSVEP-BCI systems, necessitating further research to optimize designs for clinical applications.

Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces

Introduction

Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples.

Methods

To address these challenges, this study proposes a transfer learning-based approach that uses a convolutional neural network for high-level feature extraction, followed by Euclidean space data alignment to ensure similar distributions of extracted features. Furthermore, a source selection technique based on the Euclidean distance metric was applied to measure the distance between each source feature sample and a reference point from the target domain. The samples with the lowest distance were then chosen to increase the similarity between source and target datasets. Finally, the transferred features are applied to a discriminative restricted Boltzmann machine classifier for P300 detection.

Results

The proposed method was evaluated on the state-of-the-art BCI Competition III dataset II and rapid serial visual presentation dataset. The results demonstrate that the proposed technique achieves an average accuracy of 97% for both online and offline after 15 repetitions, which is comparable to the state-of-the-art methods. Notably, the proposed approach requires <½ of the training samples needed by previous studies.

Discussion

Therefore, this technique offers an efficient solution for developing ERP-based BCIs with robust performance against reduced a number of training data.

Evaluation of Different Types of Stimuli in an Event-Related Potential-Based Brain-Computer Interface Speller under Rapid Serial Visual Presentation

Rapid serial visual presentation (RSVP) is currently a suitable gaze-independent paradigm for controlling visual brain-computer interfaces (BCIs) based on event-related potentials (ERPs), especially for users with limited eye movement control. However, unlike gaze-dependent paradigms, gaze-independent ones have received less attention concerning the specific choice of visual stimuli that are used. In gaze-dependent BCIs, images of faces-particularly those tinted red-have been shown to be effective stimuli. This study aims to evaluate whether the colour of faces used as visual stimuli influences ERP-BCI performance under RSVP. Fifteen participants tested four conditions that varied only in the visual stimulus used: grey letters (GL), red famous faces with letters (RFF), green famous faces with letters (GFF), and blue famous faces with letters (BFF). The results indicated significant accuracy differences only between the GL and GFF conditions, unlike prior gaze-dependent studies. Additionally, GL achieved higher comfort ratings compared with other face-related conditions. This study highlights that the choice of stimulus type impacts both performance and user comfort, suggesting implications for future ERP-BCI designs for users requiring gaze-independent systems.

Evaluation of Single-Trial Classification to Control a Visual ERP-BCI under a Situation Awareness Scenario

An event-related potential (ERP)-based brain-computer interface (BCI) can be used to monitor a user's cognitive state during a surveillance task in a situational awareness context. The present study explores the use of an ERP-BCI for detecting new planes in an air traffic controller (ATC). Two experiments were conducted to evaluate the impact of different visual factors on target detection. Experiment 1 validated the type of stimulus used and the effect of not knowing its appearance location in an ERP-BCI scenario. Experiment 2 evaluated the effect of the size of the target stimulus appearance area and the stimulus salience in an ATC scenario. The main results demonstrate that the size of the plane appearance area had a negative impact on the detection performance and on the amplitude of the P300 component. Future studies should address this issue to improve the performance of an ATC in stimulus detection using an ERP-BCI.

Research on Lower Limb Step Speed Recognition Method Based on Electromyography

Wearable exoskeletons play an important role in people's lives, such as helping stroke and amputation patients to carry out rehabilitation training and so on. How to make the exoskeleton accurately judge the human action intention is the basic requirement to ensure that it can complete the corresponding task. Traditional exoskeleton control signals include pressure values, joint angles and acceleration values, which can only reflect the current motion information of the human lower limbs and cannot be used to predict motion. The electromyography (EMG) signal always occurs before a certain movement; it can be used to predict the target's gait speed and movement as the input signal. In this study, the generalization ability of a BP neural network and the timing property of a hidden Markov chain are used to properly fuse the two, and are finally used in the research of this paper. Experiments show that, using the same training samples, the recognition accuracy of the three-layer BP neural network is only 91%, while the recognition accuracy of the fusion discriminant model proposed in this paper can reach 95.1%. The results show that the fusion of BP neural network and hidden Markov chain has a strong solving ability for the task of wearable exoskeleton recognition of target step speed.

Assessment of Different Feature Extraction Methods for Discriminating Expressed Emotions during Music Performance towards BCMI Application

A Brain-Computer Music Interface (BCMI) system may be designed to harness electroencephalography (EEG) signals for control over musical outputs in the context of emotionally expressive performance. To develop a real-time BCMI system, accurate and computationally efficient emotional biomarkers should first be identified. In the current study, we evaluated the ability of various features to discriminate between emotions expressed during music performance with the aim of developing a BCMI system. EEG data was recorded while subjects performed simple piano music with contrasting emotional cues and rated their success in communicating the intended emotion. Power spectra and connectivity features (Magnitude Square Coherence (MSC) and Granger Causality (GC)) were extracted from the signals. Two different approaches of feature selection were used to assess the contribution of neutral baselines in detection accuracies; 1- utilizing the baselines to normalize the features, 2- not taking them into account (non-normalized features). Finally, the Support Vector Machine (SVM) has been used to evaluate and compare the capability of various features for emotion detection. Best detection accuracies were obtained from the non-normalized MSC-based features equal to 85.57 ± 2.34, 84.93 ± 1.67, and 87.16 ± 0.55 for arousal, valence, and emotional conditions respectively, while the power-based features had the lowest accuracies. Both connectivity features show acceptable accuracy while requiring short processing time and thus are potential candidates for the development of a real-time BCMI system.

A Personalized Compression Method for Steady-State Visual Evoked Potential EEG Signals

As an informative electroencephalogram (EEG) signal, steady-state visual evoked potential (SSVEP) stands out from many paradigms for application in wireless wearable devices. However, its data are usually enormous, occupy too many bandwidth sources and require immense power when transmitted in the raw data form, so it is necessary to compress the signal. This paper proposes a personalized EEG compression and reconstruction algorithm for the SSVEP application. In the algorithm, to realize personalization, a primary artificial neural network (ANN) model is first pre-trained with the open benchmark database towards BCI application (BETA). Then, an adaptive ANN model is generated with incremental learning for each subject to compress their individual data. Additionally, a personalized, non-uniform quantization method is proposed to reduce the errors caused by compression. The recognition accuracy only decreases by 3.79% when the compression rate is 12.7 times, and is tested on BETA. The proposed algorithm can reduce signal loss by from 50.43% to 81.08% in the accuracy test compared to the case without ANN and uniform quantization.

Control of a Robotic Arm With an Optimized Common Template-Based CCA Method for SSVEP-Based BCI

Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently.

Parameter Estimation for Hindmarsh–Rose Neurons

In the paper, a new adaptive model of a neuron based on the Hindmarsh–Rose third-order model of a single neuron is proposed. The learning algorithm for adaptive identification of the neuron parameters is proposed and analyzed both theoretically and by computer simulation. The proposed algorithm is based on the Lyapunov functions approach and reduced adaptive observer. It allows one to estimate parameters of the population of the neurons if they are synchronized. The rigorous stability conditions for synchronization and identification are presented.

Secret-Key Agreement by Asynchronous EEG over Authenticated Public Channels

In this paper, we propose a new system for a sequential secret key agreement based on 6 performance metrics derived from asynchronously recorded EEG signals using an EMOTIV EPOC+ wireless EEG headset. Based on an extensive experiment in which 76 participants were engaged in one chosen mental task, the system was optimized and rigorously evaluated. The system was shown to reach a key agreement rate of 100%, a key extraction rate of 9%, with a leakage rate of 0.0003, and a mean block entropy per key bit of 0.9994. All generated keys passed the NIST randomness test. The system performance was almost independent of the EEG signals available to the eavesdropper who had full access to the public channel.

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

Embedded Brain Computer Interface: State-of-the-Art in Research

There is a wide area of application that uses cerebral activity to restore capabilities for people with severe motor disabilities, and actually the number of such systems keeps growing. Most of the current BCI systems are based on a personal computer. However, there is a tremendous interest in the implementation of BCIs on a portable platform, which has a small size, faster to load, much lower price, lower resources, and lower power consumption than those for full PCs. Depending on the complexity of the signal processing algorithms, it may be more suitable to work with slow processors because there is no need to allow excess capacity of more demanding tasks. So, in this review, we provide an overview of the BCIs development and the current available technology before discussing experimental studies of BCIs.