Optimization of SSVEP brain responses with application to eight-command Brain–Computer Interface

Objective. Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) allow healthy subjects to communicate. However, their dependence on gaze control prevents their use with severely disabled patients. Gaze-independent SSVEP-BCIs have been designed but have shown a drop in accuracy and have not been tested in brain-injured patients. In the present paper, we propose a novel independent SSVEP-BCI based on covert attention with an improved classification rate. We study the influence of feature extraction algorithms and the number of harmonics. Finally, we test online communication on healthy volunteers and patients with locked-in syndrome (LIS). Approach. Twenty-four healthy subjects and six LIS patients participated in this study. An independent covert two-class SSVEP paradigm was used with a newly developed portable light emitting diode-based 'interlaced squares' stimulation pattern. Main results. Mean offline and online accuracies on healthy subjects were respectively 85 ± 2% and 74 ± 13%, with eight out of twelve subjects succeeding to communicate efficiently with 80 ± 9% accuracy. Two out of six LIS patients reached an offline accuracy above the chance level, illustrating a response to a command. One out of four LIS patients could communicate online. Significance. We have demonstrated the feasibility of online communication with a covert SSVEP paradigm that is truly independent of all neuromuscular functions. The potential clinical use of the presented BCI system as a diagnostic (i.e., detecting command-following) and communication tool for severely brain-injured patients will need to be further explored.

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“…Using higher stimulation frequencies (i.e. 30-60 Hz) are less epileptogenic but reduce SSVEP amplitudes [41].…”

Section: Discussionmentioning

confidence: 99%

An independent SSVEP-based brain–computer interface in locked-in syndrome

et al. 2014

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“…SSVEPs are a phase-locked brain response triggered by fixing the user's gaze upon a repetitive visual stimulus (RVS) such as a flashing light [8], or a reversing pattern [9]. The SSVEP is produced by groups of neurons which output a repetitive signal matching the RVS frequency, maintained for the duration of the fixation period.…”

Section: Introductionmentioning

confidence: 99%

Improving SSVEP-BCI performance using pre-trial normalization methods

Henshaw

Liu

Romano

2017

2017 8th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)

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Abstract-A brain-computer interface (BCI) enables users to communicate through a computer using only their brain signals, by extracting brain signal features containing information representative of the user's intent, and can be used in a wide variety of areas such as entertainment, rehabilitation, or assistive technologies. In this paper, two novel normalization methods are assessed with the aim of improving the quality of the extracted features: Baseline-Corrected canonical correlation analysis (BC-CCA), and Scaled CCA. Both methods are found to be able to improve classification accuracy in conditions using frequencies with a large range, whilst BC-CCA is the superior of the two, improving SSVEP detection accuracy by as much as 9.22%.

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“…Recently, thanks to dramatic advances in technologies for recording human brain activity and methods for statistical pattern recognition, neuroscientists have recognized the possibility of decoding the human mind based on brain activity recorded via multiple neuroimaging modalities, such as functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS), electroencephalography (EEG), and magnetoencephalography (MEG) [1][2][3][4][5][6][7][8][9][10][11][12][13]. These "mind reading" or "brain reading" technologies have been explored not only to advance our understanding of neural information processing but also to develop new applications such as braincomputer interfaces (BCI) [2,3,6,11,12], lie detection [1,5,7,8,13], and communication with severely locked-in patients [4,9,10].…”

Section: Introductionmentioning

confidence: 99%

“…These "mind reading" or "brain reading" technologies have been explored not only to advance our understanding of neural information processing but also to develop new applications such as braincomputer interfaces (BCI) [2,3,6,11,12], lie detection [1,5,7,8,13], and communication with severely locked-in patients [4,9,10].…”

Section: Introductionmentioning

confidence: 99%

Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

Hwang¹,

Lim²,

Im³

2012

Journal of Biomedical Engineering Research

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Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (± 9.64%) was attained for the proposed method; whereas an average accuracy of 57% (± 5.68%) was attained for the conventional method (significance was assessed by the one-tail paired t-test, p < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

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Optimization of SSVEP brain responses with application to eight-command Brain–Computer Interface

Cited by 166 publications

References 15 publications

An independent SSVEP-based brain–computer interface in locked-in syndrome

An independent SSVEP-based brain–computer interface in locked-in syndrome

Improving SSVEP-BCI performance using pre-trial normalization methods

Classification of Mental States Based on Spatiospectral Patterns of Brain Electrical Activity

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