Multi-phase cycle coding for SSVEP based brain-computer interfaces

Tong, Jijun; Zhu, Danhua

doi:10.1186/1475-925x-14-5

Cited by 22 publications

(9 citation statements)

References 34 publications

(35 reference statements)

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“…The technology of brain–computer interface (BCI) has recently gained increasing attentions and has great potentials in improving the quality of life for people suffering from severe motor disabilities, such as cerebral palsy and paralysis [ 1 ]. The goal of BCI is to establish a communication channel between human brains and ambient environment [ 2 ], by directly decoding brain signals in order to control external devices.…”

Section: Introductionmentioning

confidence: 99%

Combining multiple features for error detection and its application in brain–computer interface

et al. 2016

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BackgroundBrain–computer interface (BCI) is an assistive technology that conveys users’ intentions by decoding various brain activities and translating them into control commands, without the need of verbal instructions and/or physical interactions. However, errors existing in BCI systems affect their performance greatly, which in turn confines the development and application of BCI technology. It has been demonstrated viable to extract error potential from electroencephalography recordings.MethodsThis study proposed a new approach of fusing multiple-channel features from temporal, spectral, and spatial domains through two times of dimensionality reduction based on neural network. 26 participants (13 males, mean age = 28.8 ± 5.4, range 20–37) took part in the study, who engaged in a P300 speller task spelling cued words from a 36-character matrix. In order to evaluate the generalization ability across subjects, the data from 16 participants were used for training and the rest for testing.ResultsThe total classification accuracy with combination of features is 76.7 %. The receiver operating characteristic (ROC) curve and area under ROC curve (AUC) further indicate the superior performance of the combination of features over any single features in error detection. The average AUC reaches 0.7818 with combined features, while 0.7270, 0.6376, 0.7330 with single temporal, spectral, and spatial features respectively.ConclusionsThe proposed method combining multiple-channel features from temporal, spectral, and spatial domain has better classification performance than any individual feature alone. It has good generalization ability across subject and provides a way of improving error detection, which could serve as promising feedbacks to promote the performance of BCI systems.

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Section: Introductionmentioning

confidence: 99%

Combining multiple features for error detection and its application in brain–computer interface

et al. 2016

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“…Recently, individual calibration data have been incorporated into target identification approaches to improve the performance of SSVEP-based BCIs (29)(30)(31)(32). By incorporating individual difference of SSVEPs in target identification, these methods all achieved significantly improved classification performance.…”

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confidence: 99%

High-speed spelling with a noninvasive brain–computer interface

Chen

Wang

Nakanishi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

727

663

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The past 20 years have witnessed unprecedented progress in braincomputer 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.brain-computer interface | electroencephalogram | steady-state visual evoked potentials | joint frequency-phase modulation B rain-computer interfaces (BCIs), which can provide a new communication channel to humans, have received increasing attention in recent years (1, 2). Among various applications, BCI spellers (3-9) are especially valuable because they can help patients with severe motor disabilities (e.g., amyotrophic lateral sclerosis, stroke, and spinal cord injury) communicate with other people. Currently, electroencephalogram (EEG) is the most popular method of implementing BCI spellers due to its noninvasiveness, simple operation, and relatively low cost. However, low signal-to-noise ratio (SNR) of the scalp-recorded EEG signals and lack of computationally efficient solutions in EEG modeling limit the information transfer rates (ITRs) of EEGbased BCI spellers to ∼1.0 bits per second (bps) (1, 4). For example, the well-known P300 speller proposed by Farwell and Donchin (5) can spell up to five letters per minute (∼0.5 bps). Until recently few studies using visual evoked potentials (VEPs) demonstrated higher ITRs of 1.7-2.4 bps (6, 7). In contrast, the invasive BCI spellers in humans and monkeys show higher performance. For example, the P300 speller with electrocorticogram recordings obtained a peak ITR of 1.9 bps in a human subject (8). A recent monkey study on keyboard neural prosthesis using multineuron recordings reported an ITR up to 3.5 bps (9). Although communication speed of the EEG-based spellers has been significantly improved in the past decade (4), it still remains a key obstacle to real-life applications in humans.Recently, the BCI speller using steady-state VEPs (SSVEPs) has attracted increasing attention due to its high communication rate and little user training (4, 10, 11). An SSVEP speller typically uses SSVEPs to detect the user's gaze direction to a target character (10). Although the SSVE...

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“…Compared to other types of BCIs, such a high number of possible targets is a unique feature of the EEG2Code BCI. There are other approaches increasing the number of targets, like using multiple m-sequences in cVEP BCIs [ 8 , 9 ] or by using sequential coding strategy in SSVEP BCIs [ 10 , 11 ], but both are still limited and there is, to the best of our knowledge, no method allowing a virtually unlimited number of possible targets (e.g. 96.3% accuracy for 500,000 targets with a trial length of 2 s for the best subject S1).…”

Section: Discussionmentioning

confidence: 99%

“…Another approach is to group several targets and use multiple m-sequences for each group [ 8 , 9 ], but this approach is also limited as the number of equal-sized m-sequences is limited. In case of SSVEP BCIs there are also some approaches to increase the number of targets, for example by using multiple frequencies sequential coding [ 10 ] or by using multi-phase cycle coding [ 11 ], but both show reduced information transfer rates.…”

Section: Introductionmentioning

confidence: 99%

Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface

Nagel

Spüler

2018

PLoS ONE

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Visual evoked potentials (VEPs) can be measured in the EEG as response to a visual stimulus. Commonly, VEPs are displayed by averaging multiple responses to a certain stimulus or a classifier is trained to identify the response to a certain stimulus. While the traditional approach is limited to a set of predefined stimulation patterns, we present a method that models the general process of VEP generation and thereby can be used to predict arbitrary visual stimulation patterns from EEG and predict how the brain responds to arbitrary stimulation patterns. We demonstrate how this method can be used to model single-flash VEPs, steady state VEPs (SSVEPs) or VEPs to complex stimulation patterns. It is further shown that this method can also be used for a high-speed BCI in an online scenario where it achieved an average information transfer rate (ITR) of 108.1 bit/min. Furthermore, in an offline analysis, we show the flexibility of the method allowing to modulate a virtually unlimited amount of targets with any desired trial duration resulting in a theoretically possible ITR of more than 470 bit/min.

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Multi-phase cycle coding for SSVEP based brain-computer interfaces

Cited by 22 publications

References 34 publications

Combining multiple features for error detection and its application in brain–computer interface

Combining multiple features for error detection and its application in brain–computer interface

High-speed spelling with a noninvasive brain–computer interface

Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed Brain-Computer Interface

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