Low-Latency Test-Time Adaptation for Inter-Subject SSVEP Decoding via Online Euclidean Alignment and Frequency-Regularized Entropy Minimization

Steady state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has gained a lot of attention due to its robustness and high information transfer rate (ITR). However, transitioning well-controlled laboratory-oriented BCI demonstrations to real-world applications poses severe challenges for this exciting field. For instance, conducting BCI experiments usually requires skilled technicians to abrade the area of skin underneath each electrode and apply an electrolytic gel or paste to acquire high-quality SSVEPs from hair-covered areas. Our previous proof-of-concept study has proposed an alternative approach that employed electroencephalographic signals collected from easily accessible non-hair-bearing areas including neck, behind the ears, and face to realize an SSVEP-based BCI. The study results showed that, with proper electrode placements and advanced signal-processing algorithms, the SSVEPs measured from non-hair-bearing areas in off-line SSVEP experiments could achieve comparable SNR to that obtained from the hair-bearing occipital areas. This study extended the previous work to systematically investigate the costs and benefits of non-hair SSVEPs. Furthermore, this study developed and evaluated an online BCI system based solely on non-hair EEG signals. A 12-target identification task was employed to quantitatively assess the performance of the online SSVEP-based BCI system. All subjects successfully completed the tasks using non-hair SSVEPs with 84.08 ± 15.60% averaged accuracy and 30.21 ± 10.61 bits/min averaged ITR. The empirical results of this study demonstrated the practicality of implementing an SSVEP-based BCI based on signals from non-hair-bearing areas, significantly improving the feasibility and practicality of real-world BCIs.

Electroencephalogram (EEG) based brain-computer interfaces (BCIs) for wheelchair control have great value for those with devastating neuromuscular disorders. Although there have been many attempts to implement EEG-based wheelchair control systems by P300, steady state visual evoked potential (SSVEP), and motor imagery (MI) related event-related desynchronization/synchronization (ERD/ERS), the number of simultaneous control commands in those BCI systems is strictly limited, and those BCI control do not work for a non-negligible portion of users due to the problem of BCI Illiteracy.In this paper, we develop a multimodal BCI based wheelchair control system, the user could employ subject-optimized mental strategies to produce multiple commands to control the wheelchair, which include ERD/ERS, SSVEP, and simultaneous ERD/ERS and SSVEP. It could not only help address ”BCI illiteracy”, but also provide simultaneous control commands for complex control. Experiment results demonstrate the proposed system is effective and flexible in practical application.KeywordsBCIwheelchair controlmultimodalBCI Illiteracy

This paper presents the major challenges and solutions for the Brain Computer Interfaces (BCIs), which are based on Steady State Visual Evoked Potentials (SSVEPs). A BCI utilizes the information transmitted by the brain; there are several methods available for analyzing these brain activities. One of the major challenges in BCI is to remove the noise successfully. Averaging along with Wavelets has been proposed in this paper for de-noising the brain activities. Moreover, three different approaches have been investigated to estimate the frequency spectrum of the brain signals. In addition to the well known Fourier Transform (FT) technique, for spectral estimation, there are many parametric and non parametric techniques for computing the frequency content of a signal. This paper presents a comparison between Fast Fourier Transform (FFT), MUltiple SIgnal Classification (MUSIC) and Linear Predictive Coding (LPC). The effectiveness of different techniques has been studied and the simulation results have shown that MUSIC outperforms the other approaches.

A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system relies on the photic driving response to effectively elicit characteristic electroencephalogram (EEG) signals. However, traditional visual stimuli mainly adopt high-contrast black-and-white flickering stimulations, which are easy to cause visual fatigue. This paper presents an SSVEP dataset acquired at a wide frequency range from 1 to 60 Hz with an interval of 1 Hz using flickering stimuli under two different modulation depths. This dataset contains 64-channel EEG data from 30 healthy subjects when they fixated on a single flickering stimulus. The stimulus was rendered on an LCD display with a refresh rate of 240 Hz. Initially, the dataset was rigorously validated through comprehensive data analysis to investigate SSVEP responses and user experiences. Subsequently, BCI performance was evaluated through offline simulations of frequency-coded and phase-coded BCI paradigms. This dataset provides comprehensive and high-quality data for studying and developing SSVEP-based BCI systems.

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can provide relatively easy, reliable and high speed communication. However, the performance is still not satisfactory, especially in some users who are not able to generate strong enough SSVEP signals. This work aims to strengthen a user’s SSVEP by alpha down-regulating neurofeedback training (NFT) and consequently improve the performance of the user in using SSVEP-based BCIs. Approach. An experiment with two steps was designed and conducted. The first step was to investigate the relationship between the resting alpha activity and the SSVEP-based BCI performance, in order to determine the training parameter for the NFT. Then in the second step, half of the subjects with ‘low’ performance (i.e. BCI classification accuracy <80%) were randomly assigned to a NFT group to perform a real-time NFT, and the rest half to a non-NFT control group for comparison. Main results. The first step revealed a significant negative correlation between the BCI performance and the individual alpha band (IAB) amplitudes in the eyes-open resting condition in a total of 33 subjects. In the second step, it was found that during the IAB down-regulating NFT, on average the subjects were able to successfully decrease their IAB amplitude over training sessions. More importantly, the NFT group showed an average increase of 16.5% in the SSVEP signal SNR (signal-to-noise ratio) and an average increase of 20.3% in the BCI classification accuracy, which was significant compared to the non-NFT control group. Significance. These findings indicate that the alpha down-regulating NFT can be used to improve the SSVEP signal quality and the subjects’ performance in using SSVEP-based BCIs. It could be helpful to the SSVEP related studies and would contribute to more effective SSVEP-based BCI applications.

The electroencephalogram (EEG) based Brain-Computer Interfaces (BCIs) has been considerably extended to the multidisciplinary and challenging nature of BCI study. The algorithms of Digital signal processing and pattern recognition certainly constitute necessary components of a BCI system which are applied to the EEG signals to decode mental states those are relevant for BCI operation. The basic BCI concepts such as: brain activity monitoring, BCI operation, and the relevant mental states for BCI are applied to WSN. The major types of relevant mental states for BCI, namely motor imagery (ERD/ERS), steady state visual evoked potentials (SSVEP) are also mapped to MANET’s behaviour. The multivariate nature of the EEG combined with the neuroscience familiarity on hemispheric brain specialization is favourably taken into account to derive optimal combinations of the individual signals composing the EEG. BCI provides a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological process of the brain function. A necessary factor in the successful operation of BCI systems is the methods used to process the brain signals. The processed data has been applied on to nodes of MANET where each set of sensor nodes are used for particular application. Based on the output of BCI commands, the MANET is operated and controlled with using only activities of neurons of brain, hence this approach could be more helpful to physically differently abled people or medically injured people.

Steady-state visual evoked potential (SSVEP)-based brain– computer interfaces (BCIs) have been widely studied. Considerable progress has been made in the aspects of stimulus coding, electroencephalogram processing, and recognition algorithms to enhance system performance. The properties of SSVEP have been demonstrated to be highly sensitive to stimulus luminance. However, thus far, there have been very few reports on the impact of background luminance on the system performance of SSVEP-based BCIs. This study investigated the impact of stimulus background luminance on SSVEPs. Specifically, this study compared two types of background luminance, i.e., (1) black luminance [red, green, blue (rgb): (0, 0, 0)] and (2) gray luminance [rgb: (128, 128, 128)], and determined their effect on the classification performance of SSVEPs at the stimulus frequencies of 9, 11, 13, and 15 Hz. The offline results from nine healthy subjects showed that compared with the gray background luminance, the black background luminance induced larger SSVEP amplitude and larger signal-to-noise ratio, resulting in a better classification accuracy. These results suggest that the background luminance of visual stimulus has a considerable effect on the SSVEP and therefore has a potential to improve the BCI performance.

This paper presents an electroencephalogram (EEG) based brain computer interface (BCI) design as a control mechanism for the Open Vibe Space shooter game. BCI using Steady State Visual Evoked Potential (SSVEP) is designed. The SSVEP is generated in response to a flickering visual stimulus. The setup uses different flickering frequencies which are displayed on a Liquid Crystal Display (LCD) monitor to elicit SSVEP responses. The EEG signals are recorded over the visual cortex of the brain. These signals are pre-processed to remove undesired information. To extract features, the power information contained in the corresponding frequency bands are calculated. The feature set is fed as an input to the classifier to identify the target class. This study tried to compare the performance of different classification algorithms in terms of accuracy, information transfer rate and the response time. Based on the outcome, support vector machine classifier with radial basis function recorded the best average accuracy of 90%.

Currently, the operational electroencephalography (EEG)-based brain–computer interfaces (BCIs) suffer from problems of BCI latency/lag issues, which restricts the use of interfaces impractical scenarios. One of the reasons behind the present challenges is the application of a purely data-driven approach to the BCI pipeline. Although BCI applications have improved significantly with the research in the fields of artificial intelligence (AI) and machine learning (ML), fundamental issues of data-driven training restrict the latency that can be achieved under current BCI paradigms. This work explores the possibility of future BCI using a combination of data-driven and theory-driven methods. In this study, an EEG-BCI dataset from steady-state visually evoked potentials (SSVEPs) is applied, where the SSVEP signals contain, source components from the occipital, parietal and frontal regions of the brain. Source reconstruction is done with the combination of independent component analysis (ICA) and low-resolution electromagnetic tomography analysis (LORETA). This method was able to predict BCI classification labels 5[Formula: see text]s earlier, based on pre-recorded signals from the scalp. The novelty of the current contribution lies in utilizing the source reconstructed EEG time-series for BCI classification, which allows for retention of classification accuracy up to 70% while working with the reduced data dimensionality. Implementation of this algorithm will allow a significant reduction in lag in online BCIs.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to realize high-speed communication between the human brain and the external environment. Recently, multiple access (MA) methods in telecommunications have been introduced into the system design of BCIs and showed their potential in improving BCI performance. This study investigated the feasibility of hybrid frequency and phase coding methods in multi-target SSVEP-based BCIs. Specifically, this study compared two hybrid target-coding strategies: (1) mixed frequency and phase coding, and (2) joint frequency and phase coding. In a simulated online BCI experiment using a 40-target BCI speller, BCI performance for both coding approaches were tested with a group of six subjects. At a spelling speed of 40 characters per minute (1.5 seconds per character), both approaches obtained high information transfer rates (ITR) (mixed coding: 172.37±28.67 bits/min, joint coding: 170.94±28.32 bits/min) across subjects. There was no statistically significant difference between the two approaches (p>0.05). These results suggest that the hybrid frequency and phase coding methods are highly efficient for multi-target coding in SSVEP BCIs with a large number of classes, providing a practical solution to implement a high-speed BCI speller.

The performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) has been drastically improved in the past few years. In conventional SSVEP-based BCIs, the speed of a selection is fixed towards high performance based on preliminary offline analysis. However, due to inter-trial variability, the optimal selection time to achieve sufficient accuracy is different for each trial. To optimize the performance of SSVEP-based BCIs, this study proposed a dynamic stopping method that can adaptively determine a selection time in each trial by applying a threshold to the probability of detecting a target. A 12-class SSVEP dataset recorded from 10 subjects was used to evaluate the performance of the proposed method. Compared to the conventional method with a fixed selection time towards the highest accuracy, the proposed method could significantly reduce the averaged selection time (0.84±0.39 s vs. 1.44±0.63 s, p<;0.05) with comparable accuracy (99.44±1.57 % vs. 99.55±1.22 %). As a result, the simulated online information transfer rate (ITR) with the dynamic stopping method achieved a significant improvement compared to the conventional method (125.30±21.55 bits/min vs. 92.75±23.77 bits/min). These results suggest that the proposed dynamic stopping method is effective for improving the performance of SSVEP-based BCI systems.

Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems suffer from low SSVEP response intensity and visual fatigue, resulting in lower accuracy when operating the system for continuous commands, such as an electric wheelchair control. This study proposes two SSVEP improvements to create a practical BCI for communication and control in disabled people. The first is flicker pattern modification for increasing SSVEP response through mixing (1) fundamental and first harmonic frequencies, and (2) two fundamental frequencies for an additional number of commands. The second method utilizes a quick response (QR) code for visual stimulus patterns to increase the SSVEP response and reduce visual fatigue. Eight different stimulus patterns from three flickering frequencies (7, 13, and 17 Hz) were presented to twelve participants for the test and score levels of visual fatigue. Two popular SSVEP methods, i.e., power spectral density (PSD) with Welch periodogram and canonical correlation analysis (CCA) with overlapping sliding window, are used to detect SSVEP intensity and response, compared to the checkerboard pattern. The results suggest that the QR code patterns can yield higher accuracy than checkerboard patterns for both PSD and CCA methods. Moreover, a QR code pattern with low frequency can reduce visual fatigue; however, visual fatigue can be easily affected by high flickering frequency. The findings can be used in the future to implement a real-time, SSVEP-based BCI for verifying user and system performance in actual environments.

This study proposes a steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) independent of amplitude-frequency and phase calibrations. Six stepping delay flickering sequences (SDFSs) at 32-Hz flickering frequency were used to implement a six-command BCI system. EEG signals recorded from Oz position were first filtered within 29-35 Hz, segmented based on trigger events of SDFSs to obtain SDFS epochs, and then stored separately in epoch registers. An epoch-average process suppressed the inter-SDFS interference. For each detection point, the latest six SDFS epochs in each epoch register were averaged and the normalized power of averaged responses was calculated. The visual target that induced the maximum normalized power was identified as the visual target. Eight subjects were recruited in this study. All subjects were requested to produce the "563241" command sequence four times. The averaged accuracy, command transfer interval, and information transfer rate (mean ± std.) values for all eight subjects were 97.38 ± 5.97%, 3.56 ± 0.68 s, and 42.46 ± 11.17 bits/min, respectively. The proposed system requires no calibration in either the amplitude-frequency characteristic or the reference phase of SSVEP which may provide an efficient and reliable channel for the neuromuscular disabled to communicate with external environments.

Traditional steady state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) encode visual targets mainly with different stimulation frequencies. However, as more and more visual targets are squeezed into the limited stimulation spectrum in order to improve the BCIs' communication rate, the distinguishability among the neighboring targets with adjacent stimulation frequencies is to certain extent compromised. To solve this problem, in this paper a novel joint modulation scheme is proposed for the SSVEP-based BCIs, In the new scheme, visual targets are jointly encoded with different frequencies and different spatial forms. In this way, better classification performance can be achieved, especially for the neighboring targets. A four-target prototype system is developed and tested. The validity of the proposed new system using joint modulation is verified.

We present results of a classification improvement approach for a code–modulated visual evoked potential (cVEP) based brain–computer interface (BCI) paradigm using four high–frequency flashing stimuli. Previously published research reports presented successful BCI applications of canonical correlation analysis (CCA) to steady–state visual evoked potential (SSVEP) BCIs. Our team already previously proposed the combined CCA and cVEP techniques’ BCI paradigm. The currently reported study presents the further enhanced results using a support vector machine (SVM) method in application to the cVEP–based BCI.