How to Reduce Classification Error in ERP-Based BCI: Maximum Relative Areas as a Feature for P300 Detection

Changoluisa, Vinicio; Varona, Pablo; Rodríguez, Francisco B.

doi:10.1007/978-3-319-59147-6_42

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…This methodology can contribute to subject adaptation and handles variability across sessions, as we discuss below. Our methodology takes advantage of low computational cost measures such as the area under the curve (AUC) applied to the EEG signal, which is widely used and well received in the ERP research community [3], [27]- [29]. In our approach, this measure is continuously calculated and used to characterize positive and negative ERPs throughout the epoch, thus keeping track of their temporal and spatial information.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

Changoluisa

Varona²,

Rodrı́guez³

2020

IEEE Access

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Event-related potentials (ERPs) are important neurophysiological markers widely used in scientific, medical and engineering contexts. Proper ERP detection contributes to widening the scope of use and, in general, improving functionality. The morphology and latency of ERPs are variable among subject sessions, which complicates their detection. Although variability is an intrinsic feature of neuronal activity, it can be addressed with novel views on ERP detection techniques. In this paper, we propose an agile method for characterizing and thus detecting variable ERPs, which keeps track of their temporal and spatial information through the continuous measurement of the area under the curve in ERP components. We illustrate the usefulness of the proposed ERP characterization for electrode selection in brain-computer interfaces (BCIs) and compare the results with other standard methods. We assess ERP classification for BCI use with Bayesian linear discriminant analysis (BLDA) and cross-validation. We also evaluate performance with both the information transfer rate and BCI utility. The results of our validation tests show that this characterization helps to take advantage of the information on the evolution of positive and negative ERP components and, therefore, to efficiently select electrodes for optimized ERP detection. The proposed method improves the classification accuracy and bitrate of all sets of electrodes analyzed. Furthermore, the method is robust between different day sessions. Our work contributes to the efficient detection of ERPs, manages inter-and intrasubject variability, decreases the computational cost of classic detection methods and contributes to promoting low-cost personalized brain-computer interfaces.

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Section: Introductionmentioning

confidence: 99%

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

Changoluisa

Varona²,

Rodrı́guez³

2020

IEEE Access

Self Cite

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“…The main challenges in P300 detection are variability and low Signal to Noise Ratio (SNR); therefore, several methods have been introduced to improve distinguishing P300 from other parts of EEG signal [3,6]. Averaging is a simple method, which obtains the higher detection rate by increasing the SNR, but it reduces the bit rate and deforms the ERP waveform [7].…”

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confidence: 99%

A New Method to Improve the Performance of Deep Neural Networks in Detecting P300 Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm

2021

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Background: Deep neural networks have been widely used in detection of P300 signal in Brain Machine Interface (BMI) systems which are rely on Event-Related Potentials (ERPs) (i.e. P300 signals). Such networks have high curvature variation in their error surface hampering their favorable performance. Therefore, the variations in curvature of the error surface must be minimized to improve the performance of these networks in detecting P300 signals. Objective: The aim of this paper is to introduce a method for minimizing the curvature of the error surface during training Convolutional Neural Network (CNN). The curvature variation of the error surface is highly dependent on model parameters of deep neural network; therefore, we try to minimize this curvature by optimizing the model parameters. Material and Methods: In this experimental study an attempt is made to tune the CNN parameters affecting the curvature of its error surface in order to obtain the best possible learning. For achieving this goal, Genetic Algorithm is utilized to optimize the above parameters in order to minimize the curvature variations. Results: The performance of the proposed algorithm was evaluated on EPFL dataset. The obtained results demonstrated that the proposed method detected the P300 signals with maximally 98.91% classification accuracy and 98.54% True Positive Ratio (TPR). Conclusion: The obtained results showed that using genetic algorithm for minimizing curvature of the error surface in CNN increased its accuracy in parallel with decreasing the variance of the results. Consequently, it may be concluded that the proposed method has considerable potential to be used as P300 detection module in BMI applications.

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A Fine Dry-Electrode Selection to Characterize Event-Related Potentials in the Context of BCI

Changoluisa

Varona

Rodríguez

2021

Advances in Computational Intelligence

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How to Reduce Classification Error in ERP-Based BCI: Maximum Relative Areas as a Feature for P300 Detection

Cited by 4 publications

References 15 publications

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

A Low-Cost Computational Method for Characterizing Event-Related Potentials for BCI Applications and Beyond

A New Method to Improve the Performance of Deep Neural Networks in Detecting P300 Signals: Optimizing Curvature of Error Surface Using Genetic Algorithm

A Fine Dry-Electrode Selection to Characterize Event-Related Potentials in the Context of BCI

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