An Investigation of the Spatial Properties of Steady-state Visual Evoked Potentials around the Ear

Published: Nov. 8, 2024

Language: Английский

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Enhancing SSVEP Identification with Less Individual Calibration Data Using Periodically Repeated Component Analysis

Published: Aug. 16, 2023

<p> Spatial filtering and template matching-based methods are commonly used to identify the stimulus frequency from multichannel EEG signals in steady-state visually evoked potentials (SSVEP)-based brain-computer interfaces (BCIs). However, these require sufficient calibration data obtain reliable spatial filters SSVEP templates, they underperform identification with small-sample-size data, especially when a single trial of is available for each frequency. In contrast state-of-the-art task-related component analysis (TRCA)-based methods, which construct templates based on inter-trial components SSVEP, this study proposes method called periodically repeated (PRCA), constructs maximize reproducibility across periods synthetic by replicating (PRCs). We also introduced PRCs into two improved variants TRCA. Performance evaluation was conducted using self-collected 16-target dataset public 40-target dataset. The proposed show significant improvements less training can achieve comparable performance baseline 5 trials 2 or 3 trials. Using frequency, PRCA-based achieved highest average accuracies over 95% 90% 1-s length maximum information transfer rates 198.8±57.3 bits/min 191.2±48.1 sets, respectively. Our results demonstrate effectiveness robustness reduced effort suggest its potential practical applications SSVEP-BCIs. </p>

Language: Английский

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Optimizing SSVEP-based BCI training through Adversarial Generative Neural Networks

International Journal of Electrical and Computer Engineering Research, Journal Year: 2023, Volume and Issue: 3(4), P. 8 - 14

Published: Dec. 15, 2023

Brain-computer interfaces (BCIs) based on steady-state visually evoked potential (SSVEP) use brain activity to control external devices, with applications ranging from assistive technologies gaming. Typically, BCI systems are developed using supervised learning techniques that require labelled signals. However, acquiring these signals can be tiring and time-consuming, especially for subjects disabilities. In this study, we evaluated the performance impact of synthetic train calibrate an SSVEP-based system. Specifically, used generative adversarial networks (GANs) synthesize SSVEP information, considering cases two four visual stimuli. Four scenarios different proportions real vs. were evaluated: Scenario 1 (baseline) only data Scenarios 2-4 10%, 20% 30% replaced by data, respectively. Our results reveal without a loss across tested when stimuli average reduction compared baseline 7% (Scenario 2), 10,3% 3) 9,3% 4) Furthermore, each recording has duration 2 seconds, replacing there is immediate time-saving 48 s 96 in stimuli, This trade-off between accuracy efficiency significant implications improving usability accessibility BCI, applications.

Language: Английский

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Enhancing SSVEP Identification with Less Individual Calibration Data Using Periodically Repeated Component Analysis

Published: July 20, 2023

<p> Spatial filtering and template matching-based methods are commonly used to identify the stimulus frequency from multichannel EEG signals in steady-state visually evoked potentials (SSVEP)-based brain-computer interfaces (BCIs). However, these require sufficient calibration data obtain reliable spatial filters SSVEP templates, they underperform identification with small-sample-size data, especially when a single trial of is available for each frequency. In contrast state-of-the-art task-related component analysis (TRCA)-based methods, which construct templates based on inter-trial components SSVEP, this study proposes method called periodically repeated (PRCA), constructs maximize reproducibility across periods synthetic by replicating (PRCs). We also introduced PRCs into two improved variants TRCA. Performance evaluation was conducted using self-collected 16-target dataset public 40-target dataset. The proposed show significant improvements less training can achieve comparable performance baseline 5 trials 2 or 3 trials. Using frequency, PRCA-based achieved highest average accuracies over 95% 90% 1-s length maximum information transfer rates 198.8±57.3 bits/min 191.2±48.1 sets, respectively. Our results demonstrate effectiveness robustness reduced effort suggest its potential practical applications SSVEP-BCIs. </p>

Language: Английский

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Enhancing SSVEP Identification with Less Individual Calibration Data Using Periodically Repeated Component Analysis

Published: July 20, 2023

<p> Spatial filtering and template matching-based methods are commonly used to identify the stimulus frequency from multichannel EEG signals in steady-state visually evoked potentials (SSVEP)-based brain-computer interfaces (BCIs). However, these require sufficient calibration data obtain reliable spatial filters SSVEP templates, they underperform identification with small-sample-size data, especially when a single trial of is available for each frequency. In contrast state-of-the-art task-related component analysis (TRCA)-based methods, which construct templates based on inter-trial components SSVEP, this study proposes method called periodically repeated (PRCA), constructs maximize reproducibility across periods synthetic by replicating (PRCs). We also introduced PRCs into two improved variants TRCA. Performance evaluation was conducted using self-collected 16-target dataset public 40-target dataset. The proposed show significant improvements less training can achieve comparable performance baseline 5 trials 2 or 3 trials. Using frequency, PRCA-based achieved highest average accuracies over 95% 90% 1-s length maximum information transfer rates 198.8±57.3 bits/min 191.2±48.1 sets, respectively. Our results demonstrate effectiveness robustness reduced effort suggest its potential practical applications SSVEP-BCIs. </p>

Language: Английский

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