Field Of Brain Computer Interface Research Articles

Towards interactive reinforcement learning with intrinsic feedback

Reinforcement learning (RL) and brain–computer interfaces (BCI) have experienced significant growth over the past decade. With rising interest in human-in-the-loop (HITL), incorporating human input with RL algorithms has given rise to the sub-field of interactive RL. Adjacently, the field of BCI has long been interested in extracting informative brain signals from neural activity for use in human–computer interactions. A key link between these fields lies in the interpretation of neural activity as feedback such that interactive RL approaches can be employed. We denote this new and emerging medium of feedback as intrinsic feedback. Despite intrinsic feedback’s ability to be conveyed automatically and even unconsciously, proper exploration surrounding this key link has largely gone unaddressed by both communities. Thus, to help facilitate a deeper understanding and a more effective utilization, we provide a tutorial-style review covering the motivations, approaches, and open problems of intrinsic feedback and its foundational concepts.

Augmented-reality based brain-computer interface of robot control

Brain Computer Interface (BCI) is a new approach to human-computer interaction. It can control the external devices directly with the brain without words and body movements. Brain-controlled robot is a major research area in the field of BCI, which organically integrates BCI with robotic systems to achieve safe and effective real-time control of robots using the user's electroencephalogram (EEG). Currently, there are two types of control methods for brain-controlled robots. One is direct control and the other is shared control. Direct brain control has its shortcomings, namely, low control efficiency and easy user fatigue. Shared control technique can effectively improve the control of brain-controlled robots and reduce the thinking ability of brain-controlled robots, thus making it the main control method of brain-controlled robots. The brain-computer collaborative control system based on augmented reality (AR) technology studied in this paper is a human-computer shared control method. In the experimental analysis of virtual reality (VR) systems and AR systems, this paper processes polylines through a series of control vertices with specific coordinates, using the relative distance measured between each point and the starting point as the relative coordinates, and calculates the operational errors of the two types of systems. In the system error of machining broken lines, when the relative coordinates are (10, 20), (40, 50), and (70, 80), the error values of the VR system are 0.17 mm, 0.36 mm, and 0.55 mm, respectively, while the error values of the AR system are 0.11 mm, 0.24 mm, and 0.41 mm, respectively. Therefore, the studies have illustrated the importance of AR systems for the study of brain-computer collaborative control of robots.

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.

Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application. In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied. The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%. The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.

A coarse-to-fine adaptive spatial–temporal graph convolution network with residuals for motor imagery decoding from the same limb

In the field of Brain Computer Interface (BCI) technology, Motor Imagery (MI) plays an important role as a paradigm. One of the primary focuses of this research area lies in exploring the MI of various upper limbs. Decoding MI signals from distinct joints within the same limb poses more intricate challenges in comparison to decoding MI signals originating from different upper limbs. In order to explore more efficient decoding methods, we propose a novel coarse-to-fine classification approach to investigate categorical decoding across three tasks, namely ‘rest’, ‘hand’, and ‘elbow’. This approach consists of two classification stages performed from coarse to fine. In the coarse classification stage, in order to capture the features of both resting state and the moving state in the temporal domain, the EEGNet network with temporal domain convolution is used to extract temporal domain features and classify the original samples into categories of ‘rest’ and ‘move’ (‘hand’, ‘elbow’). In the fine classification stage, the samples of ‘move’ category are segmented by time to form a graph sequence. Then, an adaptive spatial–temporal graph convolutional network with residuals is utilized to extract both temporal and spatial domains’ features from the graph sequence. The proposed algorithm has been validated experimentally on the MI-2 dataset and compared with contemporary methods. Its classification performance is quantified by the average accuracy which achieves a value of 72.21%. Extensive experimental results indicate that the novel coarse-to-fine classification approach is superior to the single classification approach.

Classification of EEG Signals Based on Sparrow Search Algorithm-Deep Belief Network for Brain-Computer Interface.

In brain-computer interface (BCI) systems, challenges are presented by the recognition of motor imagery (MI) brain signals. Established recognition approaches have achieved favorable performance from patterns like SSVEP, AEP, and P300, whereas the classification methods for MI need to be improved. Hence, seeking a classification method that exhibits high accuracy and robustness for application in MI-BCI systems is essential. In this study, the Sparrow search algorithm (SSA)-optimized Deep Belief Network (DBN), called SSA-DBN, is designed to recognize the EEG features extracted by the Empirical Mode Decomposition (EMD). The performance of the DBN is enhanced by the optimized hyper-parameters obtained through the SSA. Our method's efficacy was tested on three datasets: two public and one private. Results indicate a relatively high accuracy rate, outperforming three baseline methods. Specifically, on the private dataset, our approach achieved an accuracy of 87.83%, marking a significant 10.38% improvement over the standard DBN algorithm. For the BCI IV 2a dataset, we recorded an accuracy of 86.14%, surpassing the DBN algorithm by 9.33%. In the SMR-BCI dataset, our method attained a classification accuracy of 87.21%, which is 5.57% higher than that of the conventional DBN algorithm. This study demonstrates enhanced classification capabilities in MI-BCI, potentially contributing to advancements in the field of BCI.

Wearable brain-computer interface technology and its application

Wearable Brain-Computer Interfaces (BCIs) signify a critical evolution in human-machine communication, driven by the convergence of neuroscience, engineering, and information technology. With applications that span across industrial, medical, and recreational domains, BCIs hold potential to redefine our interaction with the technological landscape. This manuscript elucidates this transformative juncture, bifurcating into passive and active BCIs. In passive BCIs, innovations leveraging Virtual Reality (VR) and Augmented Reality (AR) are delineated, progress has been demonstrated in the classification of passively induced emotional signals, highlighting the emergence of hands-free control systems like quadcopter control and industrial inspections. The discourse on active BCIs unveils advancements such as real-time handwriting and speech decoding, driver drowsiness detection, and emotion recognition, aided by machine learning techniques. Despite groundbreaking progress, challenges in algorithm optimization, adaptability, multimodal signal complexity, and ethics persist. Future directions emphasize the potential of deep learning and multimode input signals collaboration. The manuscript underscores the societal implications, particularly in rehabilitation, communication, and entertainment. The review, therefore, serves as both an appraisal and a roadmap for the burgeoning field of wearable BCIs, underlining its role as a pathway to enhance human capabilities and quality of life.

Signature methods for brain-computer interfaces

Brain-computer interfaces (BCIs) allow direct communication between one’s central nervous system and a computer without any muscle movement hence by-passing the peripheral nervous system. They can restore disabled people’s ability to interact with their environment, e.g. communication and wheelchair control. However, to this day their performance is still hindered by the non-stationarity of electroencephalography (EEG) signals, as well as their susceptibility to noise from the users’ environment and from their own physiological activity. Moreover, a non-negligible amount of users struggle to use BCI systems based on motor imagery. In this paper, a new method based on the path signature is introduced to tackle this problem by using features which are different from the usual power-based ones. The path signature is a series of iterated integrals computed from a multidimensional path. It is invariant under translation and time reparametrization, which makes it a robust feature for multichannel EEG time series. The performance can be further boosted by combining the path signature with the gold standard Riemannian classifier in the BCI field exploiting the geometric structure of symmetric positive definite (SPD) matrices. The results obtained on publicly available datasets show that the signature method is more robust to inter-user variability than classical ones, especially on noisy and low-quality data. Hence, this study paves the way towards the use of mathematical tools that until now have been neglected, in order to tackle the EEG-based BCI variability issue. It also sheds light on the lead-lag relationship captured by path signature which seems relevant to assess the underlying neural mechanisms.

Brain computer interfaces in computer science and engineering areas: a systematic study

<span>Brain-computer interfaces (BCI) are a channel that implements direct communication between the brain and some external unit. Developments of BCIs can provide new application opportunities in a large number of fields of use. In the development of BCI devices, the development of technology and digital technology represented a big change, as it provided the necessary computing power to implement and run the continuously developing signal processing algorithms that ensure processing and evaluation. The aim of this paper is to provide an overview of BCI research results which were published in the engineering field. In the present study, articles that had a greater impact, where the annual average number of citations is greater than 30, in the BCI field were reviewed and processed in a systematic way, in order to make individual research more comparable. The systematic processing was focused on the aims of application, used device/ dataset, applied data process and achieved best accuracy. This systematic study summarizes the most effective methods used in the BCI processing and highlights the future trends. The results showed an accuracy of 85% thanks to increasingly reliable, accurate and cost-effective signal detection and processing devices, as well as algorithms.</span>

IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification

As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.

Current Trends, Challenges, and Future Research Directions of Hybrid and Deep Learning Techniques for Motor Imagery Brain–Computer Interface

The field of brain–computer interface (BCI) enables us to establish a pathway between the human brain and computers, with applications in the medical and nonmedical field. Brain computer interfaces can have a significant impact on the way humans interact with machines. In recent years, the surge in computational power has enabled deep learning algorithms to act as a robust avenue for leveraging BCIs. This paper provides an up-to-date review of deep and hybrid deep learning techniques utilized in the field of BCI through motor imagery. It delves into the adoption of deep learning techniques, including convolutional neural networks (CNNs), autoencoders (AEs), and recurrent structures such as long short-term memory (LSTM) networks. Moreover, hybrid approaches, such as combining CNNs with LSTMs or AEs and other techniques, are reviewed for their potential to enhance classification performance. Finally, we address challenges within motor imagery BCIs and highlight further research directions in this emerging field.

Multimodal Social Data Analytics on the Design and Implementation of an EEG-Mechatronic System Interface

The devices that can read Electroencephalography (EEG) signals have been widely used for Brain-Computer Interfaces (BCIs). Popularity in the field of BCIs has increased in recent years with the development of several consumer-grade EEG devices that can detect human cognitive states in real-time and deliver feedback to enhance human performance. Several previous studies have been conducted to understand the fundamentals and essential aspects of EEG in BCIs. However, the significant issue of how consumer-grade EEG devices can be used to control mechatronic systems effectively has been given less attention. In this article, we have designed and implemented an EEG BCI system using the OpenBCI Cyton headset and a user interface running a game to explore the concept of streamlining the interaction between humans and mechatronic systems with a BCI EEG-mechatronic system interface. Big Multimodal Social Data (BMSD) analytics can be applied to the high-frequency and high-volume EEG data, allowing us to explore aspects of data acquisition, data processing, and data validation and evaluate the Quality of Experience (QoE) of our system. We employ real-world participants to play a game to gather training data that was later put into multiple machine learning models, including a linear discriminant analysis (LDA), k-nearest neighbours (KNN), and a convolutional neural network (CNN). After training the machine learning models, a validation phase of the experiment took place where participants tried to play the same game but without direct control, utilising the outputs of the machine learning models to determine how the game moved. We find that a CNN trained to the specific user was able to control the game and performed with the highest activation accuracy from the machine learning models tested, along with the highest user-rated QoE, which gives us significant insight for future implementation with a mechatronic system.

A spatiotemporal symmetrical transformer structure for EEG emotion recognition

In the field of brain-computer interface, automatic recognition of emotions based on electroencephalogram (EEG) signals is of great significance. At present, deep learning can deeply mine the information in the data, especially the convolutional neural network (CNN) and the recurrent neural network (LSTM), which has remarkably improved the accuracy in numerous fields, hence it is applied by researchers to EEG-based emotional identification research. Nonetheless, existing CNN and LSTM-based models still rely on data preprocessing and feature extraction. Furthermore, CNNs have limitations in perceiving global dependencies while LSTMs have problems such as vanishing gradients in the case of long sequences. In this paper, we proposed the Spatiotemporal Symmetric Transformer Model (STS-Transformer), an effective EEG emotion recognition model, to overcome the current problems. STS-Transformer is an end-to-end framework that directly recognizes emotion from raw EEG signals without data preprocessing and feature extraction. This method achieves 89.86% and 86.83% accuracy respectively in the binary classification of valence and arousal in the DEAP dataset; in the DREAMER dataset, the binary classification accuracy of valence and arousal is 85.09% and 82.32%. Therefore, our method exhibits remarkable advantages over other end-to-end models in similar studies.

Authentication using c-VEP evoked in a mild-burdened cognitive task.

In recent years, more and more researchers are devoting themselves to the studies about authentication based on biomarkers. Among a wide variety of biomarkers, code-modulated visual evoked potential (c-VEP) has attracted increasing attention due to its significant role in the field of brain-computer interface. In this study, we designed a mild-burdened cognitive task (MBCT), which can check whether participants focus their attention on the visual stimuli that evoke c-VEP. Furthermore, we investigated the authentication based on the c-VEP evoked in the cognitive task by introducing a deep learning method. Seventeen participants were recruited to take part in the MBCT experiments including two sessions, which were carried out on two different days. The c-VEP signals from the first session were extracted to train the authentication deep models. The c-VEP data of the second session were used to verify the models. It achieved a desirable performance, with the average accuracy and F1 score, respectively, of 0.92 and 0.89. These results show that c-VEP carries individual discriminative characteristics and it is feasible to develop a practical authentication system based on c-VEP.

Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs.

In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.

Brain Computer Interfaces: The Future of Communication Between the Brain and the External World

This paper provides a comprehensive review of the current state of research on Brain Computer Interfaces (BCIs) and their potential applications. The objective of this study was to gather information from various sources, including journal articles, conference papers, and books, to analyze the advancements and limitations of BCIs. A systematic literature review was conducted using databases such as PubMed, IEEE Xplore, and Google Scholar, with specific keywords related to BCIs and their applications. The selected studies were thoroughly analyzed to identify common themes, methodologies, and key findings. The main contributions of this review include an overview of different types of BCIs, their applications in fields such as medicine, entertainment, and education, and the challenges and limitations faced by BCI technology. The findings highlight the potential of BCIs in restoring motor function, improving the quality of life for individuals with various conditions, and enhancing human-technology interaction. Furthermore, the review identifies future research directions, including signal processing improvements, exploring hybrid and multimodal approaches, conducting long-term real-world studies, addressing ethical considerations, and prioritizing user-centred design. This comprehensive analysis of existing literature provides valuable insights for researchers and practitioners in the field of BCIs and sets the stage for future advancements in this rapidly evolving domain.

A case for hybrid BCIs: combining optical and electrical modalities improves accuracy.

Near-infrared spectroscopy (NIRS) is a promising research tool that found its way into the field of brain-computer interfacing (BCI). BCI is crucially dependent on maximized usability thus demanding lightweight, compact, and low-cost hardware. We designed, built, and validated a hybrid BCI system incorporating one optical and two electrical modalities ameliorating usability issues. The novel hardware consisted of a NIRS device integrated with an electroencephalography (EEG) system that used two different types of electrodes: Regular gelled gold disk electrodes and tri-polar concentric ring electrodes (TCRE). BCI experiments with 16 volunteers implemented a two-dimensional motor imagery paradigm in off- and online sessions. Various non-canonical signal processing methods were used to extract and classify useful features from EEG, tEEG (EEG through TCRE electrodes), and NIRS. Our analysis demonstrated evidence of improvement in classification accuracy when using the TCRE electrodes compared to disk electrodes and the NIRS system. Based on our synchronous hybrid recording system, we could show that the combination of NIRS-EEG-tEEG performed significantly better than either single modality only.

MI-CAT: A transformer-based domain adaptation network for motor imagery classification

Due to its convenience and safety, electroencephalography (EEG) data is one of the most widely used signals in motor imagery (MI) brain–computer interfaces (BCIs). In recent years, methods based on deep learning have been widely applied to the field of BCIs, and some studies have gradually tried to apply Transformer to EEG signal decoding due to its superior global information focusing ability. However, EEG signals vary from subject to subject. Based on Transformer, how to effectively use data from other subjects (source domain) to improve the classification performance of a single subject (target domain) remains a challenge. To fill this gap, we propose a novel architecture called MI-CAT. The architecture innovatively utilizes Transformer’s self-attention and cross-attention mechanisms to interact features to resolve differential distribution between different domains. Specifically, we adopt a patch embedding layer for the extracted source and target features to divide the features into multiple patches. Then, we comprehensively focus on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains. Furthermore, we also utilize two non-shared domain-based attention blocks to efficiently capture domain-dependent information, optimizing the features extracted from the source and target domains to assist in feature alignment. To evaluate our method, we conduct extensive experiments on two real public EEG datasets, Dataset IIb and Dataset IIa, achieving competitive performance with an average classification accuracy of 85.26% and 76.81%, respectively. Experimental results demonstrate that our method is a powerful model for decoding EEG signals and facilitates the development of the Transformer for brain–computer interfaces (BCIs).

A comprehensive review on motion trajectory reconstruction for EEG-based brain-computer interface.

The advance in neuroscience and computer technology over the past decades have made brain-computer interface (BCI) a most promising area of neurorehabilitation and neurophysiology research. Limb motion decoding has gradually become a hot topic in the field of BCI. Decoding neural activity related to limb movement trajectory is considered to be of great help to the development of assistive and rehabilitation strategies for motor-impaired users. Although a variety of decoding methods have been proposed for limb trajectory reconstruction, there does not yet exist a review that covers the performance evaluation of these decoding methods. To alleviate this vacancy, in this paper, we evaluate EEG-based limb trajectory decoding methods regarding their advantages and disadvantages from a variety of perspectives. Specifically, we first introduce the differences in motor execution and motor imagery in limb trajectory reconstruction with different spaces (2D and 3D). Then, we discuss the limb motion trajectory reconstruction methods including experiment paradigm, EEG pre-processing, feature extraction and selection, decoding methods, and result evaluation. Finally, we expound on the open problem and future outlooks.

EEG-Based Emotion Recognition via Neural Architecture Search

With the flourishing development of deep learning (DL) and the convolution neural network (CNN), electroencephalogram-based (EEG) emotion recognition is occupying an increasingly crucial part in the field of brain-computer interface (BCI). However, currently employed architectures have mostly been designed manually by human experts, which is a time-consuming and labor-intensive process. In this paper, we proposed a novel neural architecture search (NAS) framework based on reinforcement learning (RL) for EEG-based emotion recognition, which can automatically design network architectures. The proposed NAS mainly contains three parts: search strategy, search space, and evaluation strategy. During the search process, a recurrent network (RNN) controller is used to select the optimal network structure in the search space. We trained the controller with RL to maximize the expected reward of the generated models on a validation set and force parameter sharing among the models. We evaluated the performance of NAS on the DEAP and DREAMER dataset. On the DEAP dataset, the average accuracies reached 97.94%, 97.74%, and 97.82% on arousal, valence, and dominance respectively. On the DREAMER dataset, average accuracies reached 96.62%, 96.29% and 96.61% on arousal, valence, and dominance, respectively. The experimental results demonstrated that the proposed NAS outperforms the state-of-the-art CNN-based methods.

Review of public motor imagery and execution datasets in brain-computer interfaces.

The demand for public datasets has increased as data-driven methodologies have been introduced in the field of brain-computer interfaces (BCIs). Indeed, many BCI datasets are available in various platforms or repositories on the web, and the studies that have employed these datasets appear to be increasing. Motor imagery is one of the significant control paradigms in the BCI field, and many datasets related to motor tasks are open to the public already. However, to the best of our knowledge, these studies have yet to investigate and evaluate the datasets, although data quality is essential for reliable results and the design of subject- or system-independent BCIs. In this study, we conducted a thorough investigation of motor imagery/execution EEG datasets recorded from healthy participants published over the past 13 years. The 25 datasets were collected from six repositories and subjected to a meta-analysis. In particular, we reviewed the specifications of the recording settings and experimental design, and evaluated the data quality measured by classification accuracy from standard algorithms such as Common Spatial Pattern (CSP) and Linear Discriminant Analysis (LDA) for comparison and compatibility across the datasets. As a result, we found that various stimulation types, such as text, figure, or arrow, were used to instruct subjects what to imagine and the length of each trial also differed, ranging from 2.5 to 29 s with a mean of 9.8 s. Typically, each trial consisted of multiple sections: pre-rest (2.38 s), imagination ready (1.64 s), imagination (4.26 s, ranging from 1 to 10 s), the post-rest (3.38 s). In a meta-analysis of the total of 861 sessions from all datasets, the mean classification accuracy of the two-class (left-hand vs. right-hand motor imagery) problem was 66.53%, and the population of the BCI poor performers, those who are unable to reach proficiency in using a BCI system, was 36.27% according to the estimated accuracy distribution. Further, we analyzed the CSP features and found that each dataset forms a cluster, and some datasets overlap in the feature space, indicating a greater similarity among them. Finally, we checked the minimal essential information (continuous signals, event type/latency, and channel information) that should be included in the datasets for convenient use, and found that only 71% of the datasets met those criteria. Our attempts to evaluate and compare the public datasets are timely, and these results will contribute to understanding the dataset's quality and recording settings as well as the use of using public datasets for future work on BCIs.