A New Statistical Model of Electroencephalogram Noise Spectra for Real-Time Brain-Computer Interfaces.

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.

Brain machine interfaces (BMIs) provide one of the important means to interact with external world for a patient who lost motor function by using brain electrical signals such as scalp electroencephalogram (EEG). The main function of BMIs is to detect, analyze and classify the modulation of EEG according to a user's intent and to decode it to demonstrate to others. Most of conventional BMIs with relatively high speed and high classification accuracy have a requisite that a user can open and move his/her eyes toward a target corresponding to user's intent and gaze at it. Locked-in patients, who have lost almost all motor function including facial muscle, cannot control the eye-movement as well as gaze. The present study aimed at developing a BMI which was independent of the eye-movement and eye-gaze as a final goal. In this paper, we discuss the feasibility of binary class of BMI available with eyes-closed condition using a recent our newly observation that the steady-state visual evoked potential (SSVEP) could be modulated by performing the mental tasks even in the eyes-close state. Temporal evolution of the modulation of SSVEP was investigated using the amplitude of SSVEP and Kullback-Leibler (KL) divergence. The amplitudes of fundamental SSVEP (10 Hz) averaged across 11 participants showed significant difference between the mental focusing task and relaxed runs from the onset of the flicker stimuli with the frequency of 10 Hz and the intensity of 5 lx, whereas those of the second harmonic SSVEP (20 Hz) gave only small difference without statistical significance. For the image recalling task, the amplitude differences between the fundamental SSVEPs in the task and relaxed conditions did not show significant difference, whereas the amplitude differences of the second harmonic SSVEP showed the main effect of the image recall task. The KL divergences for the fundamental SSVEP amplitude showed large differences at the onset of flicker stimuli and gradual decrease with time at P4 and O2 for the mental focus task, yet no such characteristic evolution was not seen in KL divergence for the second harmonic SSVEP. On the other hand, the KL divergences during the image recalling were increased with time for both the fundamental and second harmonic SSVEPs at most of the electrode sites. The classification accuracies of the proposed BMI exceeded 75 % in average across the subjects for both tasks using Fisher's linear discriminant analysis.

In EEG studies, one of the most common ways to detect a weak periodic signal in the steady-state visual evoked potential (SSVEP) is spectral evaluation, a process that detects peaks of power present at notable temporal frequencies. However, the presence of noise decreases the signal-to-noise ratio (SNR), which in turn lowers the probability of successful detection of these spectral peaks. In this paper, using a single EEG channel, we compare the detection performance of four different metrics to analyse the SSVEP: two metrics that use spectral power density, and two other metrics that use phase coherency. We employ these metrics find weak signals with a known temporal frequency hidden in the SSVEP, using both simulation and real data from a stereoscopic apparent depth movement perception task. We demonstrate that out of these metrics, the phase coherency analysis is the most sensitive way to find weak signals in the SSVEP, provided that the phase information of the stimulus eliciting the SSVEP is preserved.

Steady-state visual evoked potential (SSVEP) is used for Brain-Computer Interface that requires little training for user, offers high information transfer rate and higher accuracy in living environments. To elicit an SSVEP, a Repetitive Visual Stimulus (RVS) of rectangular shape has been proposed to the subjects in different ways. RVS is rendered on a computer screen by flashing graphical patterns. The properties of SSVEP depend on the rendering device and frequency, size, shape and color of the stimuli. But, literatures on SSVEP-based BCI are seldom provided with reasons for usefulness of rendering devices or RVS properties. The aim of this research is to study the effect of all these stimulation properties on performance of SSVEP while elicited by a rectangular shape. A correlation matrix is made to help selection of any suitable SSVEP stimulator. The percentage change in energy and power from one kind of stimulation to another is also shown in this paper. Performance achieved in different cases has been compared with each other for apposite understanding. This will help a researcher to select proper stimulation types to elicit SSVEP.

This paper investigate the effectiveness of the Empirical Mode Decomposition (EMD) based Power Spectrum analysis (PSA) technique to evaluate the Performance of SSVEP based Brain computer inference (BCI) system in terms of SSEP recognition accuracy and Information transmission rate (ITR). Steady State Visual evoked Potential (SSVEP) is a quasi sinusoidal signal contaminated into recorded EEG signal. The presence of artifacts and spontaneous EEG signal deteriorate the SSVEP Performance. EMD is technique that decomposes the recorded EEG Signal into several oscillating components known as intrinsic mode functions (IMF). The selection of IMF components plays a vital role in recognizing SSVEP signal with high accuracy. Power spectrum density (PSD) as a feature is extracted from the SSVEP Prominent IMF component to recognize the accuracy of SSVEP BCI System. The obtained result compared with the Wavelet-based PSA approach and conventional PSA approach. The result obtained from four subject demonstrate that the improve the SSVEP performance in terms accuracy and ITR about 4.24% and 6.78 bits/minute as compared to DWT-PSA, 6.78% and 10.65 bits/minute as compared to standard PSA respectively.

Steady-state visual evoked potentials (SSVEPs) enable brain-computer interfaces to achieve efficient performance in command detection accuracy and information transfer rate (ITR). However, a limited bandwidth of SSVEPs causes a limited number of possible command in BCIs. Moreover since the amplitude of SSVEP at a particular frequency depends on users, some BCI commands could be executed easily (higher accuracy) and the others hardly (lower accuracy). To solve these problems, the present paper proposes a paradigm of BCI using phase-modulated visual stimuli. The commands of BCI are represented by a combination of binary codes. Two different phases of flickering stimuli correspond to binary codes. A command is determined by the sequence of binary codes which is demodulated from the phases of SSVEP. This protocol can be analogues of binary phase-shift keying (BPSK) in digital communications. Experimental results show that the proposed paradigm achieves even probabilities of command execution, although only a single electrode was used for SSVEP detection.

Objective. Recent studies exploit the neural signal recorded via electroencephalography (EEG) to get a more objective measurement of perceived video quality. Most of these studies capitalize on the event-related potential component P3. We follow an alternative approach to the measurement problem investigating steady state visual evoked potentials (SSVEPs) as EEG correlates of quality changes. Unlike the P3, SSVEPs are directly linked to the sensory processing of the stimuli and do not require long experimental sessions to get a sufficient signal-to-noise ratio. Furthermore, we investigate the correlation of the EEG-based measures with the outcome of the standard behavioral assessment. Approach. As stimulus material, we used six gray-level natural images in six levels of degradation that were created by coding the images with the HM10.0 test model of the high efficiency video coding (H.265/MPEG-HEVC) using six different compression rates. The degraded images were presented in rapid alternation with the original images. In this setting, the presence of SSVEPs is a neural marker that objectively indicates the neural processing of the quality changes that are induced by the video coding. We tested two different machine learning methods to classify such potentials based on the modulation of the brain rhythm and on time-locked components, respectively. Main results. Results show high accuracies in classification of the neural signal over the threshold of the perception of the quality changes. Accuracies significantly correlate with the mean opinion scores given by the participants in the standardized degradation category rating quality assessment of the same group of images. Significance. The results show that neural assessment of video quality based on SSVEPs is a viable complement of the behavioral one and a significantly fast alternative to methods based on the P3 component.

Brain-computer interfaces (BCIs) based on steady-state visual evoked potential (SSVEP) have attracted much attention due to their high efficiency and accuracy. The SSVEP paradigm and decoding methods have been extensively studied and achieved remarkable results. This study proposed two modulation methods for the SSVEP paradigm, namely color inversion modulation and brightness compression modulation. Color inversion modulation adjusts the stimulus to adapt to the changing background, while brightness compression modulation ensures high contrast by reducing the background brightness. Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. MTSGNN is built with efficient convolutional structures and uses global average pooling to achieve classification, which effectively reduces the risk of model overfitting on small EEG datasets and improves classification performance. We conduct experiments to evaluate the performance of the proposed modulation and decoding methods. Compared with color inversion modulation and no modulation, the brightness compression modulation method achieved the best performance. In addition, MTSGNN outperforms the best competitive decoding method by 11.98%, 3.9% and 5.15% under color inversion modulation, brightness compression modulation and no modulation, respectively. The experimental results demonstrate the effectiveness of the proposed modulation methods and the robustness of the proposed decoding method. This study significantly improves the performance of SSVEP in dynamic backgrounds and effectively expands the practical application scenarios of BCI.

Over the years, the driver-vehicle interface has been improved, but interacting with in-vehicle features can still increase distraction and affect road safety. This study aims to introduce brain-machine interface (BMI)- based solution to potentially enhance road safety. To achieve this goal, we evaluated visual stimuli properties (SPs) for a steady state visually evoked potentials (SSVEP)-based BMI system. We used a heads-up display (HUD) as the primary screen to present icons for controlling in-vehicle functions such as music, temperature, settings, and navigation. We investigated the effect of various SPs on SSVEP detection performance including the duty cycle and signal-to-noise ratio of visual stimuli, the size, color, and frequency of the icons, and array configuration and location. The experiments were conducted with 10 volunteers and the signals were analyzed using the canonical correlation analysis (CCA), filter bank CCA (FBCCA), and power spectral density analysis (PSDA). Our experimental results suggest that stimuli with a green color, a duty cycle of 50%, presented at a central location, with a size of 36 cm2 elicit a significantly stronger SSVEP response and enhanced SSVEP detection time. We also observed that lower SNR stimuli significantly affect SSVEP detection performance. There was no statistically significant difference observed in SSVEP response between the use of an LCD monitor and a HUD.

Brain–computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) have been well researched due to their easy system configuration, little or no user training and high information transfer rates. To elicit an SSVEP, a repetitive visual stimulus (RVS) is presented to the user. The properties of this RVS (e.g., frequency, luminance) have a significant influence on the BCI performance and user comfort. Several studies in this area in the last one-and-half decades have focused on evaluating different stimulus parameters (i.e., properties). However, there is little research on the synthesis of the existing studies, as the last review on the subject was published in 2010. Consequently, we conducted a scoping review of related studies on the influence of stimulus parameters on SSVEP response and user comfort, analyzed them and summarized the findings considering the physiological and neurological processes associated with BCI performance. In the review, we found that stimulus type, frequency, color contrast, luminance contrast and size/shape of the retinal image are the most important stimulus properties that influence SSVEP response. Regarding stimulus type, frequency and luminance, there is a trade-off between the best SSVEP response quality and visual comfort. Finally, since there is no unified measuring method for visual comfort and a lack of differentiation in the high-frequency band, we proposed a measuring method and a division of the band. In summary, the review highlights which stimulus properties are important to consider when designing SSVEP BCIs. It can be used as a reference point for future research in BCI, as it will help researchers to optimize the design of their SSVEP stimuli.

Brain-computer interface (BCI) systems have emerged as an augmentative technology that can provide a promising solution for individuals with motor dysfunctions and for the elderly who are experiencing muscle weakness. Steady-state visually evoked potentials (SSVEPs) are widely adopted in BCI systems due to their high speed and accuracy when compared to other BCI paradigms. In this paper, we apply combined magnitude and phase features for class discrimination in a real-time SSVEP-based BCI platform. In the proposed real-time system users gain control of a motorised bed system with seven motion commands and an idle state. Experimental results amongst eight participants demonstrate that the proposed real-time BCI system can successfully discriminate between different SSVEP signals achieving high information transfer rates (ITR) of 82.73 bits/min. The attractive features of the proposed system include noninvasive recording, simple electrode configuration, excellent BCI response and minimal training requirements.

Steady-state visual evoked potential (SSVEP)-based brain-computer Interface (BCI) has demonstrated the potential to manage multi-command targets to achieve high-speed communication. Recent studies on multi-class SSVEP-based BCI have focused on synchronous systems, which rely on predefined time and task indicators; thus, these systems that use passive approaches may be less suitable for practical applications. Asynchronous systems recognize the user's intention (whether or not the user is willing to use systems) from brain activity; then, after recognizing the user's willingness, they begin to operate by switching swiftly for real-time control. Consequently, various methodologies have been proposed to capture the user's intention. However, in-depth investigation of recognition methods in asynchronous BCI system is lacking. Thus, in this work, three recognition methods (power spectral density analysis, canonical correlation analysis (CCA), and support vector machine (SVM)) used widely in asynchronous SSVEP BCI systems were explored to compare their performance. Further, we categorized asynchronous systems into two approaches (1-stage and 2-stage) based upon the recognition process's design, and compared their performance. To do so, a 40-class SSVEP dataset collected from 40 subjects was introduced. Finally, we found that the CCA-based method in the 2-stage approach demonstrated statistically significantly higher performance with a sensitivity of 97.62±02.06%, specificity of 76.50±23.50%, and accuracy of 75.59±10.09%. Thus, it is expected that the 2-stage approach together with CCA-based recognition and FB-CCA classification have good potential to be implemented in practical asynchronous SSVEP BCI systems.

The application of the brain-computer interface (BCI) is massively helpful and advantageous for disabled people. Moreover, BCI is an arrangement of software and hardware interface that provides a direct interaction between the human brain and computer devices. Therefore, in this article, A steady state visual evoked potential (SSVEP)-based BCI system is presented to identify SSVEP components from multi-channel electroencephalogram (EEG) data by minimizing background noise using an adaptive spatial filtering method. Here, the proposed adaptive spatial filtering-based SSVEP component extraction (ASFSCE) model improves reproducibility among multiple trails and identifies targets efficiently by optimizing the Eigenvalue problem. Along with that, the proposed ASFSCE model minimizes computational complexity from O(G<sup>2</sup>) to to get high target identification accuracy with faster execution. Performance results are measured using the SSVEP dataset. In this dataset, 11 subjects are used to perform experiments and 256-channel EEG data is taken. The efficiency of the proposed ASFSCE model is measured in terms of mean target detection accuracy and mean information transfer rate (ITR) in bits per minute. The average detection accuracy and ITR are evaluated by considering 23 trials for each subject. The obtained detection accuracy is 93.47% and ITR is 308.23 bpm.

Gamma oscillations weaken with age in healthy elderly in human EEG

Fuzzy ensemble system for SSVEP stimulation frequency detection using the MLR and MsetCCA