A study of classification techniques on P300 speller dataset

Sarraf, Jay; Pattnaik, Prasant Kumar

doi:10.1016/j.matpr.2021.06.110

Cited by 3 publications

(1 citation statement)

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“…For ERN and P300 signals, the EEG time series data are considered as features for classification. The most predominantly used classifiers for both ERN and P300 include Support Vector Machine's (SVM), Gaussian classifiers, and tree-based algorithms (Ventouras et al, 2011 ; Chavarriaga et al, 2014 ; Sarraf et al, 2021 ). Therefore, this study had adopted these classifiers for assessing the baseline EEG classification accuracy.…”

Section: Methodsmentioning

confidence: 99%

Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces

Singanamalla

Lin

2022

Front. Neurosci.

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Brain-computer interfaces (BCI) relying on electroencephalography (EEG) based neuroimaging mode has shown prospects for real-world usage due to its portability and optional selectivity of fewer channels for compactness. However, noise and artifacts often limit the capacity of BCI systems especially for event-related potentials such as P300 and error-related negativity (ERN), whose biomarkers are present in short time segments at the time-series level. Contrary to EEG, invasive recording is less prone to noise but requires a tedious surgical procedure. But EEG signal is the result of aggregation of neuronal spiking information underneath the scalp surface and transforming the relevant BCI task's EEG signal to spike representation could potentially help improve the BCI performance. In this study, we designed an approach using a spiking neural network (SNN) which is trained using surrogate-gradient descent to generate task-related multi-channel EEG template signals of all classes. The trained model is in turn leveraged to obtain the latent spike representation for each EEG sample. Comparing the classification performance of EEG signal and its spike-representation, the proposed approach enhanced the performance of ERN dataset from 79.22 to 82.27% with naive bayes and for P300 dataset, the accuracy was improved from 67.73 to 69.87% using xGboost. In addition, principal component analysis and correlation metrics were evaluated on both EEG signals and their spike-representation to identify the reason for such improvement.

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Section: Methodsmentioning

confidence: 99%

Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces

Singanamalla

Lin

2022

Front. Neurosci.

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EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers

Subasi,

Qaisar

2025

Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction

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An improved mayfly algorithm and its application

2022

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An improved version of the mayfly algorithm called the golden annealing crossover-mutation mayfly algorithm (GSASMA) is proposed to address the low convergence efficiency and insufficient search capability of existing mayfly algorithms. First, the speed of individual mayflies is optimized using a simulated annealing algorithm to improve the update rate. The position of individuals is improved using the golden sine algorithm. Second, the impact of using different crossover and mutation methods in the algorithm is compared, and the optimal strategy is selected from the algorithm. To evaluate the performance of the algorithm, simulation experiments were carried out for 10 different test functions, and the results were compared with those of existing algorithms. The simulation results show that the algorithm developed in this paper converges faster and the solutions obtained are closer to the global optimum. Finally, GSASMA was used to optimize a support vector machine (SVM) that was used to identify the P300 signal for five subjects. The experimental results show that the SVM optimized by the algorithm proposed in this paper has higher recognition accuracy than an extreme learning machine.

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A study of classification techniques on P300 speller dataset

Cited by 3 publications

References 30 publications

Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces

Spike-Representation of EEG Signals for Performance Enhancement of Brain-Computer Interfaces

EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiers

An improved mayfly algorithm and its application

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