Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

https://www.mdpi.com/1424-8220/19/6/1423/pdf

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges.

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

/pdf/current-status-challenges-and-possible-solutions-of-eeg-4el4wek5td.pdf

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

A review on transfer learning in EEG signal analysis

Deep learning for motor imagery EEG-based classification: A review

One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various dimensions of EEG, a novel MI classification framework is first introduced in this paper, including a new 3D representation of EEG, a multi-branch 3D convolutional neural network (3D CNN) and the corresponding classification strategy. The 3D representation is generated by transforming EEG signals into a sequence of 2D array which preserves spatial distribution of sampling electrodes. The multi-branch 3D CNN and classification strategy are designed accordingly for the 3D representation. Experimental evaluation reveals that the proposed framework reaches state-of-the-art classification kappa value level and significantly outperforms other algorithms by 50% decrease in standard deviation of different subjects, which shows good performance and excellent robustness on different subjects. The framework also shows great performance with only nine sampling electrodes, which can significantly enhance its practicality. Moreover, the multi-branch structure exhibits its low latency and a strong ability in mitigating overfitting issues which often occur in MI classification because of the small training dataset.

A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification

Successful applications of brain-computer interface (BCI) approaches to motor imagery (MI) are still limited. In this paper, we propose a classification framework for MI electroencephalogram (EEG) signals that combines a convolutional neural network (CNN) architecture with a variational autoencoder (VAE) for classification. The decoder of the VAE generates a Gaussian distribution, so it can be used to fit the Gaussian distribution of EEG signals. A new representation of input was developed by combining the time, frequency, and channel information from the EEG signal, and the CNN-VAE method was designed and optimized accordingly for this form of input. In this network, the classification of the extracted CNN features is performed via the deep network VAE. Our framework, with an average kappa value of 0.564, outperforms the best classification method in the literature for BCI Competition IV dataset 2b with a 3% improvement. Furthermore, using our own dataset, the CNN-VAE framework also yields the best performance for both three-electrode and five-electrode EEGs and achieves the best average kappa values 0.568 and 0.603, respectively. Our results show that the proposed CNN-VAE method raises performance to the current state of the art.

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.

An embedded lightweight SSVEP-BCI electric wheelchair with hybrid stimulator

The application of wireless sensors in the brain-computer interface (BCI) system provides great convenience for the acquisition of electroencephalography (EEG) signals. However, a large amount of training data is needed to build the classification architectures used in motor imagery (MI) brain-computer interface (BCI), which is time-consuming to generate. To address this issue, transfer learning has gained significant attention in a small sample setting BCI system. The transfer learning methods have shown promising results by leveraging labeled patterns from the source domain to learn robust classifiers for the target domain, which has only a limited number of labeled samples. However, the successful application of such approaches in a motor imagery BCI remains limited. In this paper, we present a novel framework called domain transfer multiple kernel boosting (DTMKB), which extends the DTMKL algorithms by applying boosting techniques for learning kernel-based classifiers with the transfer of multiple kernels. Based on the proposed framework, we examined their empirical performance in comparison to several state-of-the-art algorithms on two MI task datasets. DTMKB yields the best performance for all datasets and achieves the best average classification accuracy 87.60%, 76.00%, 74.66%, and 74.13%, respectively. In particular, the proposed framework can be applied successfully in a small sample of EEG motor imagery signals.

/pdf/domain-transfer-multiple-kernel-boosting-for-classification-2uqypz0tje.pdf

Domain Transfer Multiple Kernel Boosting for Classification of EEG Motor Imagery Signals

Brain–computer interface (BCI) is a promising intelligent healthcare technology to improve human living quality across the lifespan, which enables assistance of movement and communication, rehabilitation of exercise and nerves, monitoring sleep quality, fatigue and emotion. Most BCI systems are based on motor imagery electroencephalogram (MI-EEG) due to its advantages of sensory organs affection, operation at free will and etc. However, MI-EEG classification, a core problem in BCI systems, suffers from two critical challenges: the EEG signal’s temporal non-stationarity and the nonuniform information distribution over different electrode channels. To address these two challenges, this paper proposes TCACNet, a temporal and channel attention convolutional network for MI-EEG classification. TCACNet leverages a novel attention mechanism module and a well-designed network architecture to process the EEG signals. The former enables the TCACNet to pay more attention to signals of task-related time slices and electrode channels, supporting the latter to make accurate classification decisions. We compare the proposed TCACNet with other state-of-the-art deep learning baselines on two open source EEG datasets. Experimental results show that TCACNet achieves 11.4% and 7.9% classification accuracy improvement on two datasets respectively. Additionally, TCACNet achieves the same accuracy as other baselines with about 50% less training data. In terms of classification accuracy and data efficiency, the superiority of the TCACNet over advanced baselines demonstrates its practical value for BCI systems. 

Rui Na

Papers

EEG Classification of Motor Imagery Using a Novel Deep Learning Framework.

An embedded lightweight SSVEP-BCI electric wheelchair with hybrid stimulator

Domain Transfer Multiple Kernel Boosting for Classification of EEG Motor Imagery Signals

TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI

Cross-subject fusion based on time-weighting canonical correlation analysis in SSVEP-BCIs