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Electroencephalographic responses to periodic stimulation are termed steady-state visual evoked potentials (SSVEP). Their characteristics in terms of amplitude, frequency and phase are commonly assumed to be stationary. In this work, we tested this assumption in 30 healthy participants submitted to 50 trials of 60s flicker stimulation at 15Hz frequency. We showed that the amplitude of the first and second harmonic frequency components of SSVEP signals were in general not stable over time. The power (squared amplitude) of the fundamental component was stationary only in 30% the subjects, while the power at the second harmonic frequency was stationary in 66.7% of the group. The phases of both SSVEP frequency components were more stable over time, but could exhibit small drifts. The observed temporal changes were heterogeneous across the subjects, implying that averaging results over participants should be performed carefully. These results may contribute to improved design and analysis of experiments employing prolonged visual stimulation. Our findings offer a novel characterization of the temporal changes of SSVEP that may help to identify their physiological basis.

Brain-Computer Interface is an emerging field that focuses on transforming brain data into machine commands. EEG-based BCI is widely used due to the non-invasive nature of Electroencephalogram. Classification of EEG signals is one of the primary components in BCI applications. Steady-State Visually Evoked Potential (SSVEP) paradigms have gained importance because of lesser training time, higher precision, and improved information transfer rate compared to P300 and motor imagery paradigms. In this paper, a novel hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron classifier (APS-MLP) is proposed to improve the classification accuracy of SSVEP five classes viz. 6.66, 7.5, 8.57, 10 and 12 Hz, signals. Scaled Conjugate Gradient descent anchors the initial position of Particle Swarm Optimization. The best position, Pbest, of each particle initializes an SCG-MLP, the accuracy of APS-MLP is obtained by averaging the accuracies of each SCG-MLP. The proposed method is compared with standard classifiers namely, k-NN, SVM, LDA and MLP. In which, the proposed algorithm achieves improved training and testing accuracies of 88.69% and 95.4% respectively, which is 12–15% higher than the standard EEG-based BCI classifiers. The proposed algorithm is robust, with a Cohen’s kappa coefficient of 0.96, and will be used in applications such as motion control and improving the quality of life for people with disabilities.

A robotic arm is a mechanical device with a given number of Degrees of Freedom (DoFs) that mimics the functions of a human arm and performs any desired task, such as grasping and moving objects. Current research is directed toward the design of robots and artificial human body parts controlled by brain signals, translating human thoughts into actions. Brain-Computer Interface (BCI) systems have been used to enable people with motor disabilities to control assistive robotic equipment that replaces the lost functions. This paper presents a review of the state-of-the-art of the latest papers dealing with the control of a robotic arm based on Electroencephalogram (EEG). A comparative study of the different methods and techniques used in different blocks of the robotic arm’s noninvasive BCI controlling system is conducted. These blocks include signal acquisition using noninvasive electrodes, signal preprocessing, feature extraction, classification, and command. Additionally, this paper presents a performance comparison of the reviewed controlling systems of robotic arms using EEG signals.

We propose a complex-valued multilayer feedforward neural network classifier for decoding of phase-coded information from steady-state visual evoked potentials. To optimize the performance of the classifier we supply it with two filter-based feature selection strategies. The proposed approaches could be used for a phase-coded brain–computer interface, enabling to encode several targets using only one stimulation frequency. The proposed classifier is a multichannel one, which distinguishes our approach from the existing single-channel ones. We show that the proposed approach outperforms others in terms of accuracy and length of the data segments used for decoding. We show that the decoding based on one optimally selected channel yields an inferior performance compared to the one based on several features, which supports our argument for a multichannel approach.

Binocular rivalry is a useful experimental paradigm to investigate aspects of neocortical dynamics related to conscious perception. Frequency-tagged EEG responses to a sine-flickered visual stimulus were contrasted between episodes of perceptual dominance, i.e. conscious perception of that stimulus and perceptual nondominance, i.e. conscious perception of a rival stimulus presented at a different frequency to the other eye. The amplitude and phase distribution of the stimulus-evoked steady-state responses depended on the stimulus modulation frequency, consistent with the presence of global resonance phenomena. At the apparent global resonance frequency, conscious perception of the stimulus modulated the steady-state response over the entire array of electrodes. These effects were significant at electrodes far from the primary visual cortex, including temporal, central, and frontal electrodes. The phase structure of the steady-state response was also investigated using coherence measures. Coherence between electrodes mostly increased during conscious perception of the stimulus. Analysis of partial coherence, removing stimulus-locked responses, indicated that synchronization of each signal to the stimulus flicker at each electrode and synchronization between signals that vary with respect to the stimulus flicker at each electrode both contribute to observed increases in coherence during conscious perception. These distinct modes of synchronization may reflect two different physiological mechanisms by which sensory signals are integrated across the cerebral cortex during conscious experience.

Flickering source is an indispensable component in steady-state visual evoked potentials (SSVEPs)-based brain–computer interface (BCI), and its background severely influences the potentials evoked by the repetitive stimuli. In this paper, we investigated the problem under three different backgrounds in the context of the SSVEP-BCI-based robot car control, including black screen, static scene and dynamic scene of the environment. In the ten subjects experiment, we found significant decrease in SSVEP amplitude in dynamic scene condition compared to the reference condition black screen (p < 0.05), which resulted in classification accuracy decrease as evaluated by 10-fold cross validation. However, our proposed experiment paradigm has shown that training with static scene or dynamic scene condition could well compensate this performance drop and improve the online robot car control with real-time video feedback. The addressed problem in our application would provide some valuable suggestions when translating the SSVEP-BCI from laboratory exploration into practical usages.

The advancements in the field of brain–computer interface (BCI) are driven by the underlying motive of improving quality of life for both healthy as well as locked in subjects. Since BCI’s are based on the response of the human brain to training or external stimuli, the improvement in terms of performance can be achieved by either enhancing the subject training procedure or by improving the external stimuli to produce maximized event related potential (ERP). P300 and steady-state visually evoked potential (SSVEP) approaches have been the most common paradigms used for stimulus-based BCI’s world over. But recently, a large number of researchers are facing a problem of BCI illiteracy in subjects, where some of the subjects showed ineffective results while training with these BCI as independent stimuli. The concept of hybrid brain–computer interface (hBCI) is a step towards eradicating this problem. Our research deals with external stimuli-based ERP generation where we discuss and compare with experimentation, three different options of visual stimulus: conventional SSVEP stimulus, P300-SSVEP hybrid stimulus, distinct target colors for P300-SSVEP-based hybrid stimulus. This paper introduces a novel hBCI paradigm and discusses the validation of improved results by comparing with the already existing stimuli options. The parameters of comparison that were considered to validate our proposal were decision accuracy (Acc), information transfer rate (ITR) and false activation rate (FAR).