Deep Comparisons of Neural Networks from the EEGNet Family

Köllőd Csaba Márton; Adolf András; Iván Kristóf; Márton Gergely; Ulbert István: Deep Comparisons of Neural Networks from the EEGNet Family.
ELECTRONICS (SWITZ), 12 (12). ISSN 2079-9292 (2023)

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Folyóiratcikk

Szerző azonosítók:

Név	ORCID	MTMT szerző azonosító
Köllőd Csaba Márton	0000-0003-3817-6709	10065216
Adolf András		10069531
Iván Kristóf	0000-0003-3637-3979	10020854
Márton Gergely		10036458
Ulbert István	0000-0001-9941-9159	10001193

Absztrakt (kivonat):

A preponderance of brain–computer interface (BCI) publications proposing artificial neural networks for motor imagery (MI) electroencephalography (EEG) signal classification utilize one of the BCI Competition datasets. However, these databases encompass MI EEG data from a limited number of subjects, typically less than or equal to 10. Furthermore, the algorithms usually include only bandpass filtering as a means of reducing noise and increasing signal quality. In this study, we conducted a comparative analysis of five renowned neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, and MI-EEGNet) utilizing open-access databases with a larger subject pool in conjunction with the BCI Competition IV 2a dataset to obtain statistically significant results. We employed the FASTER algorithm to eliminate artifacts from the EEG as a signal processing step and explored the potential for transfer learning to enhance classification results on artifact-filtered data. Our objective was to rank the neural networks; hence, in addition to classification accuracy, we introduced two supplementary metrics: accuracy improvement from chance level and the effect of transfer learning. The former is applicable to databases with varying numbers of classes, while the latter can underscore neural networks with robust generalization capabilities. Our metrics indicated that researchers should not disregard Shallow ConvNet and Deep ConvNet as they can outperform later published members of the EEGNet family.

Folyóirat címe:

ELECTRONICS (SWITZ)

Megjelenés éve:

2023

Kötet:

Szám:

ISSN:

2079-9292

Intézmény:

Pázmány Péter Katolikus Egyetem

Kar:

Információs Technológiai és Bionikai Kar (2013.07.-)