Efficient Hardware and Software Co-design for EEG Signal Classification based on Extreme Learning Machine

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Title of host publication3rd International Conference on Electrical, Computer and Communication Engineering
Subtitle of host publicationECCE 2023
PublisherIEEE
Number of pages5
ISBN (Electronic)9798350345360
ISBN (Print)979-8-3503-4537-7
Publication statusPublished - 2023

Publication series

NameInternational Conference on Electrical, Computer and Communication Engineering, ECCE

Conference

Title3rd International Conference on Electrical, Computer & Communication Engineering (ECCE 2023)
PlaceBangladesh
CityChittagong
Period23 - 25 February 2023

Abstract

ElectroEncephaloGram (EEG) is associated with multiple functions, including communications with neurons, organic monitoring, and interactions with external stimuli. By decoding EEG signals, certain human activities such as sleeping, brain diseases, motor imagery, movement of limbs, and others can be observed and controlled through the brain-computer interface (BCI). Therefore, it is vital to efficiently process EEG signals with a robust and accurate system to build BCI systems with powerful applications. However, as a weak biosignal, EEG demands a fast-reaction system signal processing with high accuracy and sensitivity. In this work, a hardware/software co-design network based on Extreme Learning Machine (ELM) is introduced for the classification of certain actions, motor imagery of the human brain. This system is based on an optimized Hierarchical Extreme Learning Machine (H-ELM) on the software layer. The proposed method has advantages over previous designs with an accuracy of 90.3%. It also improves the training speed by around 25X compared to conventional methods. The software model is also translated into efficient FPGA hardware design to maintain high computation efficiency and reduce power consumption for biomedical applications. © 2023 IEEE.

Research Area(s)

  • BCI, EEG, ELM, Extreme learning machine, Hard-ware/software co-design

Citation Format(s)

Efficient Hardware and Software Co-design for EEG Signal Classification based on Extreme Learning Machine. / LYU, Songyang; CHOWDHURY, Mehdi Hasan; CHEUNG, Ray C.C.
3rd International Conference on Electrical, Computer and Communication Engineering: ECCE 2023. IEEE, 2023. (International Conference on Electrical, Computer and Communication Engineering, ECCE).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review