A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.

/pdf/brain-computer-interfaces-a-review-2z42gnpdfy.pdf

Brain Computer Interfaces, a Review

Steady-state visually evoked potentials: focus on essential paradigms and future perspectives.

The past 20 years have witnessed unprecedented progress in brain-computer interfaces (BCIs). However, low communication rates remain key obstacles to BCI-based communication in humans. This study presents an electroencephalogram-based BCI speller that can achieve information transfer rates (ITRs) up to 5.32 bits per second, the highest ITRs reported in BCI spellers using either noninvasive or invasive methods. Based on extremely high consistency of frequency and phase observed between visual flickering signals and the elicited single-trial steady-state visual evoked potentials, this study developed a synchronous modulation and demodulation paradigm to implement the speller. Specifically, this study proposed a new joint frequency-phase modulation method to tag 40 characters with 0.5-s-long flickering signals and developed a user-specific target identification algorithm using individual calibration data. The speller achieved high ITRs in online spelling tasks. This study demonstrates that BCIs can provide a truly naturalistic high-speed communication channel using noninvasively recorded brain activities.

https://www.pnas.org/content/pnas/112/44/E6058.full.pdf

High-speed spelling with a noninvasive brain–computer interface

In recent years, there has been increasing interest in using steady-state visual evoked potential (SSVEP) in brain–computer interface (BCI) systems. However, several aspects of current SSVEP-based BCI systems need improvement, specifically in relation to speed, user variation and ease of use. With these improvements in mind, this paper presents an online multi-channel SSVEP-based BCI system using a canonical correlation analysis (CCA) method for extraction of frequency information associated with the SSVEP. The key parameters, channel location, window length and the number of harmonics, are investigated using offline data, and the result used to guide the design of the online system. An SSVEP-based BCI system with six targets, which use nine channel locations in the occipital and parietal lobes, a window length of 2 s and the first harmonic, is used for online testing on 12 subjects. The results show that the proposed BCI system has a high performance, achieving an average accuracy of 95.3% and an information transfer rate of 58 ± 9.6 bit min−1. The positive characteristics of the proposed system are that channel selection and parameter optimization are not required, the possible use of harmonic frequencies, low user variation and easy setup.

https://iopscience.iop.org/article/10.1088/1741-2560/6/4/046002/pdf

An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method

Objective. Recently, canonical correlation analysis (CCA) has been widely used in steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs) due to its high efficiency, robustness, and simple implementation. However, a method with which to make use of harmonic SSVEP components to enhance the CCA-based frequency detection has not been well established. Approach. This study proposed a filter bank canonical correlation analysis (FBCCA) method to incorporate fundamental and harmonic frequency components to improve the detection of SSVEPs. A 40-target BCI speller based on frequency coding (frequency range: 8–15.8 Hz, frequency interval: 0.2 Hz) was used for performance evaluation. To optimize the filter bank design, three methods (M1: sub-bands with equally spaced bandwidths; M2: sub-bands corresponding to individual harmonic frequency bands; M3: sub-bands covering multiple harmonic frequency bands) were proposed for comparison. Classification accuracy and information transfer rate (ITR) of the three FBCCA methods and the standard CCA method were estimated using an offline dataset from 12 subjects. Furthermore, an online BCI speller adopting the optimal FBCCA method was tested with a group of 10 subjects. Main results. The FBCCA methods significantly outperformed the standard CCA method. The method M3 achieved the highest classification performance. At a spelling rate of ~33.3 characters/min, the online BCI speller obtained an average ITR of 151.18 ± 20.34 bits min−1. Significance. By incorporating the fundamental and harmonic SSVEP components in target identification, the proposed FBCCA method significantly improves the performance of the SSVEP-based BCI, and thereby facilitates its practical applications such as high-speed spelling.

http://iopscience.iop.org/article/10.1088/1741-2560/12/4/046008/pdf

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain-computer interface.

This paper introduces the development of a practical brain-computer interface at Tsinghua University. The system uses frequency-coded steady-state visual evoked potentials to determine the gaze direction of the user. To ensure more universal applicability of the system, approaches for reducing user variation on system performance have been proposed. The information transfer rate (ITR) has been evaluated both in the laboratory and at the Rehabilitation Center of China, respectively. The system has been proved to be applicable to >90% of people with a high ITR in living environments.

/pdf/a-practical-vep-based-brain-computer-interface-52v0hwn687.pdf

A practical VEP-based brain-computer interface

Under conditions in which the visual system can not reconcile dissimilar images from the two eyes, perception typically alternates between the two images - a process known as binocular rivalry. This work presents a method based on the time-frequency analysis of the steady-state visual evoked potentials (SSVEP) that continuously registers the alternation in physiological dominance of the two eyes. Two images flickered at different frequencies as the visual stimulator to left and right eyes respectively so that the response can be recovered from the EEG by time-frequency analysis. The physiological dominance of each eye as evidenced by the SSVEP record correlates well with the subjects' report of perceptual dominance. This electrophysiological method appears to be suitable for measuring rivalry in non-verbal human or animals.

A study on binocular rivalry based on the steady state VEP

Binocular rivalry is a visual perceptual phenomenon which occurs when two incongruent stimuli are viewed by a subject through each eye, but only one of them is perceived at a time, with a switch in perception every few seconds, which reflects the alternation of perceptual dominance. To investigate the correlation between contrast-related perception and neural activities, the subjects' EEGs were recorded with a 64-channel electroencephalograph while experiencing binocular rivalry. A higher contrast grating flickering at one frequency was presented to one eye while a lower contrast grating flickering at a slightly different frequency was presented to the other eye. Steady-state visual evoked potentials (SSVEP) at the flickering frequencies were used to ;tag' the two stimuli. An improved inverse algorithm termed SSLOFO (standardized shrinking LORETA-FOCUSS) was used to solve this inverse problem, which acquires the spatial distribution of neural active sources from the EEG data. The result shows that activity in primary visual cortex (V1) increased when subjects perceived the higher contrast pattern and decreased when they perceived the lower contrast pattern. This paper presents a method based on EEG to investigate neural correlates of consciousness in real-time, which provides an alternative method to achieve comparable results to those based on fMRI methods.

Source estimation of contrast-related perception based on frequency-tagged binocular rivalry.

In binocular rivalry, a subject views two incongruent stimuli through each eye but consciously perceives only one stimulus at a time, with a switch in perceptual dominance every a few seconds. To locate the fusiform face area (FFA) which is a face-selective region, thirteen subjects are recorded with a 64-channel electroencephalograph while experiencing binocular rivalry. A face image flickering at one frequency is presented to one eye and a non-face image flickering at the same frequency is presented to the other eye. Steady state evoked potential (SSVEP) at the frequency is used as tags for the two stimuli. This paper uses an algorithm called standardized shrinking LORETA-FOCUSS (SSLOFO) to reconstruct face-selective sources from the EEG data. The sources are selected by comparing signal strength at the stimulus frequency during face dominance and face suppression. The results demonstrate that the face-selective region identified in this paper is consistent with FFA, as has been confirmed to be activated about twice as strongly in fMRI experiments when people view faces as when they view other kinds of objects. The present study also suggests that the method has the potential advantage of investigating neural correlates.

Localization of FFA using SSVEP-based binocular rivalry.

Under conditions in which the visual system can not reconcile dissimilar images from the two eyes, perception typically alternates between the two images - a process known as binocular rivalry. Binocular rivalry is a useful experimental paradigm for identifying aspects of neural correlates. Two images flickered at different frequencies as the visual stimulator to left and right eyes respectively so that the response can be recovered from the steady-state visual evoked potential (SSVEP) by time-frequency analysis. SSVEP-based binocular rivalry has potential advantage of registering the alternation in physiological dominance of the two eyes. However, flickering frequency greatly affects its practical applications. This paper presents a method of frequency selection to improve the applicability of SSVEP-based binocular rivalry. Optimal frequency is selected by comparing the stability and the signal-to-noise ratio of SSVEP between different frequencies. The SSVEP-based binocular rivalry with optimal flickering frequency correlates better with the subjects' report of perceptual dominance

Frequency Selection for SSVEP-based Binocular Rivalry

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