JOURNAL ARTICLE

The Research of EEG Headset of Brain–Computer Interface for Artificial Intelligence-Related Applications.

  • Published In: International Journal of Pattern Recognition & Artificial Intelligence, 2024, v. 38, n. 8. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Qi, Wen; Yu, Xi 3 of 3

Abstract

Over the past decade, a wide range of brain–computer interface (BCI) applications with artificial intelligence (AI) assistance have emerged. While using a head-mounted electroencephalogram (EEG) device for BCI application, it often requires a user to wear the device close to the scalp so that clear EEG signals can be obtained from the head directly. Those signals can be used to determine a user's mental state. Due to the head size differences between Chinese and European users, as well as the differences between Chinese users themselves, EEG headsets with fixed-electrode-point will enable some electrodes fail to capture a user's EEG data accurately. Combined with previous research and related design methodology, the design of a head-mounted EEG device is proposed in this paper. In this study, Chinese youth are regarded as the target user group. After evaluating different designs of device structures, the possible positions and movement methods of electrodes, an EEG headset with movable electrodes for young Chinese users is finally delivered. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Pattern Recognition & Artificial Intelligence. 2024/06, Vol. 38, Issue 8, p1
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2024
  • ISSN:0218-0014
  • DOI:10.1142/S0218001424590079
  • Accession Number:178418340
  • Copyright Statement:Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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