Comparison of Machine Learning and Deep Learning Approaches for Decoding Brain Computer Interface: An fNIRS Study

Jiahao Lu, Hongjie Yan, Chunqi Chang (Corresponding Author), Nizhuan Wang (Corresponding Author)

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Recently, deep learning has gained great attention in decoding the neuro-physiological signal. However, which one (classical machine learning or deep learning) has better performance for decoding the functional near-infrared spectroscopy (fNIRS) signal is still lack of full verification. Thus, in this paper, we systematically compared the performance of many classical machine learning methods and deep learning methods in fNIRS data processing for decoding the mental arithmetic task. The classical machine learning methods such as decision tree, linear discriminant analysis (LDA), support vector machine (SVM), K-Nearest Neighbor (KNN) and ensemble methods with strict feature extraction and screening, were used for performance comparison, while the long short-term memory-fully convolutional network (LSTM-FCN) method as a representative of deep leaning methods was applied. Results showed that the classification performance of SVM was the best among the classical machine learning methods, achieving that the average accuracy of the subject-related/unrelated were 91.0% and 83.0%, respectively. Furthermore, the classification accuracy of deep learning was significantly better than that of the involved classical machine learning methods, where the accuracy of deep learning could reach 95.3% with subject-related condition and 97.1% with subject-unrelated condition, respectively. Thus, this paper has totally showed the excellent performance of LSTM-FCN as a representative of deep learning in decoding brain signal from fNIRS dataset, which has outperformed many classical machine learning methods.

Original languageEnglish
Title of host publicationIntelligent Information Processing X
Subtitle of host publication11th IFIP TC 12 International Conference, IIP 2020, Hangzhou, China, July 3–6, 2020, Proceedings
EditorsZhongzhi Shi, Sunil Vadera, Elizabeth Chang
PublisherSpringer
Pages192-201
Number of pages10
ISBN (Print)9783030469306
DOIs
Publication statusPublished - 26 Jun 2020
Externally publishedYes
Event11th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2020 - Hangzhou, China
Duration: 3 Jul 20206 Jul 2020

Publication series

NameIFIP Advances in Information and Communication Technology
Volume581 AICT
ISSN (Print)1868-4238
ISSN (Electronic)1868-422X

Conference

Conference11th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2020
Country/TerritoryChina
CityHangzhou
Period3/07/206/07/20

Keywords

  • Brain computer interface (BCI)
  • Brain signal decoding
  • Classical machine learning
  • Deep learning
  • Functional near-infrared spectroscopy (fNIRS)

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management

Fingerprint

Dive into the research topics of 'Comparison of Machine Learning and Deep Learning Approaches for Decoding Brain Computer Interface: An fNIRS Study'. Together they form a unique fingerprint.

Cite this