Conference Papers Year : 2018

Convolutional Neural Networks for a Cursor Control Brain Computer Interface

Abstract

A Brain-Computer Interface (BCI) platform can be utilized by a patient to control an external device without making any overt movements. This can be beneficial to a variety of patients who suffer from paralysis, loss of limb, or neurodegenerative diseases. We decode brain signals using EEG during imagined body kinematics to control an on-screen cursor. Convolutional neural networks (CNNs) are already a popular choice for image-based learning problems and are useful in EEG applications. The major advantage of CNNs is that they can generate features from the signal automatically and do not require as much domain driven feature engineering as a traditional machine learning approach. We implement a CNN to perform multivariate regression over the EEG signal to predict intended cursor velocity.
radmp344nvu.pdf (159) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-02005325 , version 1 (18-02-2019)

Identifiers

  • HAL Id : hal-02005325 , version 1

Cite

David Saffo, Justin A Kilmarx, Soheil Borhani, Reza Abiri, Xiaopeng Zhao, et al.. Convolutional Neural Networks for a Cursor Control Brain Computer Interface. 2018 Biomedical Engineering Society (BMES) Annual Meeting, Oct 2018, Atlanta, United States. ⟨hal-02005325⟩
133 View
131 Download

Share

More