A dynamic channel selection algorithm for the classification of EEG and EMG data

Ahmed Al-Ani, Irena Koprinska, Ganesh R. Naik, Rami N. Khushaba

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)


The multichannel nature of EEG and EMG data poses a big challenge to the development of automatic EEG/EMG analysis and classification systems. Due to the 'curse of dimensionality' problem, the analysis and classification of several channels may not lead to the desired performance. Accordingly, a number of algorithms have been proposed to identify small 'static' subsets of channels that are capable of differentiating between samples of different classes. However, the identification of small subsets of relevant channels may not always be possible, where for certain applications the smaller the number of channels the less chance that sufficient information is provided. We propose in this paper a dynamic channel selection algorithm that identifies a channel (or a subset of channels) for each time segment of the signal that is relevant to the class of that particular time segment. To achieve this, we embraced the dynamic classifier selection methodology, and particularly the multiple classifier behaviour approach. Each EEG/EMG channel can be chosen to represent a certain unseen time segment of the signal based on the performance, or local accuracy, of its nearest neighbours in the set of training time segments. Results obtained using EEG data of a four-class alertness state classification problem reveal that the proposed approach is capable of achieving competitive performance compared to a traditional static channel selection based method. The algorithm also produced very encouraging results when used to classify EMG data collected from nine transradial amputees performing six classes of movements.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationVancouver
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509006199, 9781509006205
ISBN (Print)9781509006212
Publication statusPublished - 31 Oct 2016
Externally publishedYes
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016


Conference2016 International Joint Conference on Neural Networks, IJCNN 2016


  • EEG
  • EMG
  • Dynamic Channel
  • Algorithm


Dive into the research topics of 'A dynamic channel selection algorithm for the classification of EEG and EMG data'. Together they form a unique fingerprint.

Cite this