Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research.

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EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications

Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges.

The brain-computer interface (BCI) provides an alternative means to communicate and it has sparked growing interest in the past two decades Specifically, for Steady-State Visual Evoked Potential (SSVEP) based BCI, marked improvement has been made in the frequency recognition method and data sharing However, the number of pubic databases is still limited in this field Therefore, we present a BEnchmark database Towards BCI Application (BETA) in the study The BETA database is composed of 64-channel Electroencephalogram (EEG) data of 70 subjects performing a 40-target cued-spelling task The design and the acquisition of the BETA are in pursuit of meeting the demand from real-world applications and it can be used as a test-bed for these scenarios We validate the database by a series of analyses and conduct the classification analysis of eleven frequency recognition methods on BETA We recommend using the metric of wide-band signal-to-noise ratio (SNR) and BCI quotient to characterize the SSVEP at the single-trial and population levels, respectively The BETA database can be downloaded from the following link http://bcimedtsinghuaeducn/downloadhtml

/pdf/beta-a-large-benchmark-database-toward-ssvep-bci-application-1kcqz67f07.pdf

BETA: A Large Benchmark Database Toward SSVEP-BCI Application.

OBJECTIVE We presented a comparative study on the training methodologies of a convolutional neural network (CNN) for the detection of steady-state visually evoked potentials (SSVEP). Two training scenarios were also compared: user-independent (UI) training and user-dependent (UD) training. APPROACH The CNN was trained in both UD and UI scenarios on two types of features for SSVEP classification: magnitude spectrum features (M-CNN) and complex spectrum features (C-CNN). The canonical correlation analysis (CCA), widely used in SSVEP processing, was used as the baseline. Additional comparisons were performed with task-related components analysis (TRCA) and filter-bank canonical correlation analysis (FBCCA). The performance of the proposed CNN pipelines, CCA, FBCCA and TRCA were evaluated with two datasets: a seven-class SSVEP dataset collected on 21 healthy participants and a twelve-class publicly available SSVEP dataset collected on ten healthy participants. MAIN RESULTS The UD based training methods consistently outperformed the UI methods when all other conditions were the same, as one would expect. However, the proposed UI-C-CNN approach performed similarly to the UD-M-CNN across all cases investigated on both datasets. On Dataset 1, the average accuracies of the different methods for 1 s window length were: CCA: 69.1%  ±  10.8%, TRCA: 13.4%  ±  1.5%, FBCCA: 64.8%  ±  15.6%, UI-M-CNN: 73.5%  ±  16.1%, UI-C-CNN: 81.6%  ±  12.3%, UD-M-CNN: 87.8%  ±  7.6% and UD-C-CNN: 92.5%  ±  5%. On Dataset 2, the average accuracies of the different methods for data length of 1 s were: UD-C-CNN: 92.33%  ±  11.1%, UD-M-CNN: 82.77%  ±  16.7%, UI-C-CNN: 81.6%  ±  18%, UI-M-CNN: 70.5%  ±  22%, FBCCA: 67.1%  ±  21%, CCA: 62.7%  ±  21.5%, TRCA: 40.4%  ±  14%. Using t-SNE, visualizing the features extracted by the CNN pipelines further revealed that the C-CNN method likely learned both the amplitude and phase related information from the SSVEP data for classification, resulting in superior performance than the M-CNN methods. The results suggested that UI-C-CNN method proposed in this study offers a good balance between performance and cost of training data. SIGNIFICANCE The proposed C-CNN based method is a suitable candidate for SSVEP-based BCIs and provides an improved performance in both UD and UI training scenarios.

https://iopscience.iop.org/article/10.1088/1741-2552/ab6a67/pdf

Comparing user-dependent and user-independent training of CNN for SSVEP BCI.

Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to spoofing/circumvention, sensitivity to emotional and mental state, continuous nature, and cancelability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure personal identification and authentication. Although existing research on brain biometrics has obtained some intriguing insights, much work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability.

https://dl.acm.org/doi/pdf/10.1145/3230632

A Survey on Brain Biometrics

Objective Despite the vast research aimed at improving the performance of steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), several limitations exist that restrict the use of such applications for long-term users in the real-world. One of the main challenges has been to reduce training time while maintaining good BCI performance. In view of this challenge, this survey identifies and compares the different training requirements of feature extraction methods for SSVEP-based BCIs. Approach This paper reviews the various state-of-the-art SSVEP feature extraction methods that have been developed and are most widely used in the literature. Main results The main contributions compared to existing reviews are the following: (i) a detailed summary, including a brief mathematical description of each feature extraction algorithm, providing a guide to the basic concepts of the state-of-the-art techniques for SSVEP-based BCIs found in literature; (ii) a categorisation of the training requirements of SSVEP-based methods into three categories, defined as training-free methods, subject-specific and subject-independent training methods; (iii) a comparative review of the training requirements of SSVEP feature extraction methods, providing a reference for future work on SSVEP-based BCIs. Significance This review highlights the strengths and weaknesses of the three categories of SSVEP training methods. Training-free systems are more practical but their performance is limited due to inter-subject variability resulting from the complex EEG activity. Feature extraction methods that incorporate some training data address this issue and in fact have outperformed training-free methods: subject-specific BCIs are tuned to the individual yielding the best performance at the cost of long, tiring training sessions making these methods unsuitable for everyday use; subject-independent BCIs that make use of training data from various subjects offer a good trade-off between training effort and performance, making these BCIs better suited for practical use.

https://iopscience.iop.org/article/10.1088/1741-2552/aaca6e/pdf

To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs.

A steady-state visual evoked potential (SSVEP) is a neural response observed in the visual cortex evoked by repetitive visual stimulation In an SSVEP-based brain-computer interface (BCI) application various visual stimuli that induce SSVEPs at different frequencies are associated with distinct commands; a user activates a particular command by focusing on the targeted stimulus The pattern of these visual stimuli is one of the properties that affects the accuracy of SSVEP detection This work thus compares the two most common types of stimuli, which are the plain and checkerboard stimuli, for BCI systems Results showed a statistically significant 926% average increase in SSVEP classification when using a plain stimulus over a checkerboard stimulus

Comparison of plain and checkerboard stimuli for brain computer interfaces based on steady state visual evoked potentials

A real-time Brain-Computer Interface (BCI) music player based on steady-state visually evoked potentials (SSVEPs) is presented. This asynchronous BCI can distinguish up to six simultaneously occurring stimuli in addition to idle states during which the user does not want to execute a command. Distinguishing between an intended and non-intended control signal is an essential element for the seamless integration of BCIs with other user activities. The user-friendly design of the music player is based on typical smart phone applications integrated with flickering stimuli that evoke SSVEPs. In addition, the BCI requires only one bipolar EEG channel thus having an easy and quick setup time. The tests conducted by five subjects resulted in a mean kappa coefficient of 0.87 using the asynchronous BCI. This high performance demonstrates the potential of using the system as an assistive application for people suffering from motor impairments and even as an entertainment application for healthy users.

A Real-Time SSVEP-Based Brain-Computer Interface Music Player Application

The use of brain signals for person recognition has in recent years attracted considerable interest because of the increased security and privacy these can offer when compared to conventional biometric measures. The main challenge lies in extracting features from the EEG signals that are sufficiently distinct across individuals while also being sufficiently consistent across multiple recording sessions. A range of EEG phenomena including eyes open and eyes closed activity, visual evoked potentials (VEPs) through image presentation, and other mental tasks have been studied for their use in biometry.

Rosanne Zerafa

Papers

To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs.

Comparison of plain and checkerboard stimuli for brain computer interfaces based on steady state visual evoked potentials

A Real-Time SSVEP-Based Brain-Computer Interface Music Player Application

EEG-based biometry using steady state visual evoked potentials

The effect of distractors on SSVEP-based brain-computer interfaces