Work

Comparison of Cnn-based Eeg Classification in Sensor and Source Space

Year: 2024

Type: book-chapter

Abstract: Electroencephalography (EEG) is a popular tool in brain-computer interfacing (BCI), due to its unique time resolution and simplicity of application. For the design of BCIs, rapid and accurate classification algorithms are needed to classify the brain state correctly in real-time. Recent technological advancements facilitate the use of novel methods for signal processing and analysis such as real-time source estimation and classification via deep learning approaches. In this work a previously established convolutional neural network (CNN) architecture, the EEGNet, was applied to a publicly available motor imagery EEG dataset for classification of sensor measurements and source estimates that were computed with three different inverse approaches. Both for sensor signals and source estimates similar classification accuracies as in the literature could be achieved. However, no significant difference in performance between sensor and source space analysis was observed.

Source: Digital Library of the University of Innsbruck (University of Innsbruck)

DOI: 10.3217/978-3-99161-014-4-051

Authors: Maurer, Magdalena, Baumgarten, Daniel, Vorwerk, Johannes

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: green

Funder: Austrian Science Fund

Award: G4495373907

OpenAlex ID: W6889892065