Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed

Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems

Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions

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

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

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface

Objective: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain–computer interface (BCI) applications. Approach: Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results: We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance: The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI

Enhancing the Decoding Performance of Steady-State Visual Evoked Potentials based Brain-Computer Interface

Non-invasive Brain-Computer Interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) responses are the most widely used BCI. SSVEP are responses elicited in the visual cortex when a user gazes at an object flickering at a certain frequency. In this thesis, we investigate different BCI system design parameters for enhancing the detection of SSVEP such as change in inter-stimulus distance (ISD), EEG channels, detection algorithms and training methodologies. Closely placed SSVEP stimuli compete for neural representations. This influences the performance and limits the flexibility of the stimulus interface. In this thesis, we study the influence of changing ISD on the decoding performance of an SSVEP BCI. We propose: (i) a user-specific channel selection method and (ii) using complex spectrum features as input to a convolutional neural network (C-CNN) to overcome this challenge. We also evaluate the proposed C-CNN method in a user-independent (UI) training scenario as this will lead to a minimal calibration system and provide the ability to run inference in a plug-and-play mode. The proposed methods were evaluated on a 7-class SSVEP dataset collected on 21 healthy participants (Dataset 1). The UI method was also assessed on a publicly available 12-class dataset collected on 10 healthy participants (Dataset 2). We compared the proposed methods with canonical correlation analysis (CCA) and CNN classification using magnitude spectrum features (M-CNN). We demonstrated that the user-specific channel set (UC) is robust to change in ISD (viewing angles of 5.24ᵒ, 8.53ᵒ, and 12.23ᵒ) compared to the classic 3-channel set (3C - O1, O2, Oz) and 6-channel set (6C - PO3, PO4, POz, O1, O2, Oz). A significant improvement in accuracy of over 5% (p=0.001) and a reduction in variation of 56% (p=0.035) was achieved across ISDs using the UC set compared to the 3C set and 6C set. Secondly, the proposed C-CNN method obtained a significantly higher classification accuracy across ISDs and window lengths compared to M-CNN and CCA. The average accuracy of the C-CNN increased by over 12.8% compared to CCA and an increase of over 6.5% compared to the M-CNN for the closest ISD across all window lengths was achieved. Thirdly, the C-CNN method achieved the highest accuracy in both UD and UI training scenarios on both 7-class and 12-class SSVEP Datasets. The overall accuracies of the different methods for 1 s window length for Dataset 1 were: CCA: 69.1±10.8%, 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%. And for Dataset 2 were: CCA: 62.7±21.5%, UI-M-CNN: 70.5±22%, UI-C-CNN: 81.6±18%, UD-M-CNN: 82.8±16.7%, and UD-C-CNN: 92.3±11.1%. In summary, using the complex spectrum features, the C-CNN likely learned to use both frequency and phase related information to classify SSVEP responses. Therefore, the CNN can be trained independent of the ISD resulting in a model that generalizes to other ISDs. This suggests that the proposed methods are robust to changes in inter-stimulus distance for SSVEP detection and provides increased flexibility for user interface design of SSVEP BCIs for commercial applications. Finally, the UI method provides a virtually calibration free approach to SSVEP BCI

A fully-wearable non-invasive SSVEP-based BCI system enabled by AR techniques for daily use in real environment.

This thesis aims to explore the design and implementation of Brain Computer Interfaces (BCIs) specifically for non medical scenarios, and therefore to propose a solution that overcomes typical drawbacks of existing systems such as long and uncomfortable setup time, scarce or nonexistent mobility, and poor real-time performance. The research starts from the design and implementation of a plug-and-play wearable low-power BCI that is capable of decoding up to eight commands displayed on a LCD screen, with about 2 seconds of latency. The thesis also addresses the issues emerging from the usage of the BCI during a walk in a real environment while tracking the subject via indoor positioning system. Furthermore, the BCI is then enhanced with a smart glasses device that projects the BCI visual interface with augmented reality (AR) techniques, unbinding the system usage from the need of infrastructures in the surrounding environment