Assessing the quality of steady-state visual-evoked potentials for moving humans using a mobile electroencephalogram headset.

Recent advances in mobile electroencephalogram (EEG) systems, featuring non-prep dry electrodes and wireless telemetry, have enabled and promoted the applications of mobile brain-computer interfaces (BCIs) in our daily life. Since the brain may behave differently while people are actively situated in ecologically-valid environments versus highly-controlled laboratory environments, it remains unclear how well the current laboratory-oriented BCI demonstrations can be translated into operational BCIs for users with naturalistic movements. Understanding inherent links between natural human behaviors and brain activities is the key to ensuring the applicability and stability of mobile BCIs. This study aims to assess the quality of steady-state visual-evoked potentials (SSVEPs), which is one of promising channels for functioning BCI systems, recorded using a mobile EEG system under challenging recording conditions, e.g., walking. To systematically explore the effects of walking locomotion on the SSVEPs, this study instructed subjects to stand or walk on a treadmill running at speeds of 1, 2, and 3 mile (s) per hour (MPH) while concurrently perceiving visual flickers (11 and 12 Hz). Empirical results of this study showed that the SSVEP amplitude tended to deteriorate when subjects switched from standing to walking. Such SSVEP suppression could be attributed to the walking locomotion, leading to distinctly deteriorated SSVEP detectability from standing (84.87 ± 13.55%) to walking (1 MPH: 83.03 ± 13.24%, 2 MPH: 79.47 ± 13.53%, and 3 MPH: 75.26 ± 17.89%). These findings not only demonstrated the applicability and limitations of SSVEPs recorded from freely behaving humans in realistic environments, but also provide useful methods and techniques for boosting the translation of the BCI technology from laboratory demonstrations to practical applications

Bio-Inspired Filter Banks for SSVEP-based Brain-Computer Interfaces

Brain-computer interfaces (BCI) have the potential to play a vital role in future healthcare technologies by providing an alternative way of communication and control. More specifically, steady-state visual evoked potential (SSVEP) based BCIs have the advantage of higher accuracy and higher information transfer rate (ITR). In order to fully exploit the capabilities of such devices, it is necessary to understand the features of SSVEP and design the system considering its biological characteristics. This paper introduces bio-inspired filter banks (BIFB) for a novel SSVEP frequency detection method. It is known that SSVEP response to a flickering visual stimulus is frequency selective and gets weaker as the frequency of the stimuli increases. In the proposed approach, the gain and bandwidth of the filters are designed and tuned based on these characteristics while also incorporating harmonic SSVEP responses. This method not only improves the accuracy but also increases the available number of commands by allowing the use of stimuli frequencies elicit weak SSVEP responses. The BIFB method achieved reliable performance when tested on datasets available online and compared with two well-known SSVEP frequency detection methods, power spectral density analysis (PSDA) and canonical correlation analysis (CCA). The results show the potential of bio-inspired design which will be extended to include further SSVEP characteristic (e.g. time-domain waveform) for future SSVEP based BCIs.Comment: 2016 IEEE International Conference on Biomedical and Health Informatics (BHI

An Unsupervised Channel Selection Method for SSVEP-based Brain Computer Interfaces

Brain-computer interfaces (BCIs) provide an alternative communication channel for people with motor deficits that prevent normal communication. The underlying premise of a BCI is that a neuroimaging process such as electroencephalography (EEG) can be used to measure the user’s brain activity as signals. The obtained signals are analyzed to determine the user’s intended actions and a computer system can be used to replace voluntary muscle activity as a means of communication. The information transfer rate (ITR) of an algorithm used for determining the user’s intentions greatly affects the perceived practicality of the BCI system. Such algorithms are divided into two main categories, supervised and unsupervised. While the former achieves higher ITR, the latter is most useful when the user is unable to be involved in the calibration process of the BCI system. In our paper, we introduce an unsupervised algorithm for steady-state visual evoked potential (SSVEP)-based BCIs. Our algorithm works in three steps: (i) it selects multiple sets of electroencephalogram channels, then (ii) applies a feature extraction method to each one of these channel sets. As its final step, (iii) it combines the extracted features from these channel sets by performing a majority vote, yielding a classification. We evaluate the ITR attained using our proposed method on a dataset of 35 subjects using three different feature extraction methods. We then compare these results to existing methods in the literature that use a single channel set without a majority vote. The proposed method indicates an improvement for at least 7 subjects

Electroencephalogram Signal Processing For Hybrid Brain Computer Interface Systems

The goal of this research was to evaluate and compare three types of brain computer interface (BCI) systems, P300, steady state visually evoked potentials (SSVEP) and Hybrid as virtual spelling paradigms. Hybrid BCI is an innovative approach to combine the P300 and SSVEP. However, it is challenging to process the resulting hybrid signals to extract both information simultaneously and effectively. The major step executed toward the advancement to modern BCI system was to move the BCI techniques from traditional LED system to electronic LCD monitor. Such a transition allows not only to develop the graphics of interest but also to generate objects flickering at different frequencies. There were pilot experiments performed for designing and tuning the parameters of the spelling paradigms including peak detection for different range of frequencies of SSVEP BCI, placement of objects on LCD monitor, design of the spelling keyboard, and window time for the SSVEP peak detection processing. All the experiments were devised to evaluate the performance in terms of the spelling accuracy, region error, and adjacency error among all of the paradigms: P300, SSVEP and Hybrid. Due to the different nature of P300 and SSVEP, designing a hybrid P300-SSVEP signal processing scheme demands significant amount of research work in this area. Eventually, two critical questions in hybrid BCl are: (1) which signal processing strategy can best measure the user\u27s intent and (2) what a suitable paradigm is to fuse these two techniques in a simple but effective way. In order to answer these questions, this project focused mainly on developing signal processing and classification technique for hybrid BCI. Hybrid BCI was implemented by extracting the specific information from brain signals, selecting optimum features which contain maximum discrimination information about the speller characters of our interest and by efficiently classifying the hybrid signals. The designed spellers were developed with the aim to improve quality of life of patients with disability by utilizing visually controlled BCI paradigms. The paradigms consist of electrodes to record electroencephalogram signal (EEG) during stimulation, a software to analyze the collected data, and a computing device where the subject’s EEG is the input to estimate the spelled character. Signal processing phase included preliminary tasks as preprocessing, feature extraction, and feature selection. Captured EEG data are usually a superposition of the signals of interest with other unwanted signals from muscles, and from non-biological artifacts. The accuracy of each trial and average accuracy for subjects were computed. Overall, the average accuracy of the P300 and SSVEP spelling paradigm was 84% and 68.5 %. P300 spelling paradigms have better accuracy than both the SSVEP and hybrid paradigm. Hybrid paradigm has the average accuracy of 79 %. However, hybrid system is faster in time and more soothing to look than other paradigms. This work is significant because it has great potential for improving the BCI research in design and application of clinically suitable speller paradigm

Hybrid brain-computer interface for biomedical cyber-physical system application using wireless embedded EEG systems

© 2017 The Author(s). Background: One of the key challenges of the biomedical cyber-physical system is to combine cognitive neuroscience with the integration of physical systems to assist people with disabilities. Electroencephalography (EEG) has been explored as a non-invasive method of providing assistive technology by using brain electrical signals. Methods: This paper presents a unique prototype of a hybrid brain computer interface (BCI) which senses a combination classification of mental task, steady state visual evoked potential (SSVEP) and eyes closed detection using only two EEG channels. In addition, a microcontroller based head-mounted battery-operated wireless EEG sensor combined with a separate embedded system is used to enhance portability, convenience and cost effectiveness. This experiment has been conducted with five healthy participants and five patients with tetraplegia. Results: Generally, the results show comparable classification accuracies between healthy subjects and tetraplegia patients. For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with average classification accuracy at 74% and average information transfer rate (ITR) of the system of 27 bits/min. For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s

Decoding steady-state visual evoked potentials from electrocorticography

We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency-and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human subject. The paradigm is popular in EEG-based Brain Computer Interfacing where selectable targets are encoded by different frequency-and/or phase-tagged stimuli. We compare the performance of two state-of-the-art SSVEP decoders on both ECoG-and scalp-recorded EEG signals, and show that ECoG-based decoding is more accurate for very short stimulation lengths (i.e., less than 1 s). Furthermore, whereas the accuracy of scalp-EEG decoding bene fi ts from a multi-electrode approach, to address interfering EEG responses and noise, ECoG decoding enjoys only a marginal improvement as even a single electrode, placed over the posterior part of the primary visual cortex, seems to suf fi ce. This study shows, for the fi rst time, that EEG-based SSVEP decoders can in principle be applied to ECoG, and can be expected to yield faster decoding speeds using less electrodes