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Efficient Hardware Implementation of Real-Time Low-Power Movement Intention Detector System Using FFT and Adaptive Wavelet Transform.

Authors
  • Chamanzar, Alireza
  • Shabany, Mahdi
  • Malekmohammadi, Alireza
  • Mohammadinejad, Sara
Type
Published Article
Journal
IEEE Transactions on Biomedical Circuits and Systems
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
Jun 01, 2017
Volume
11
Issue
3
Pages
585–596
Identifiers
DOI: 10.1109/TBCAS.2017.2669911
PMID: 28534785
Source
Medline
License
Unknown

Abstract

The brain-computer interfacing (BCI), a platform to extract features and classify different motor movement tasks from noisy and highly correlated electroencephalogram signals, is limited mostly by the complex and power-hungry algorithms. Among different techniques recently devised to tackle this issue, real-time onset detection, due to its negligible delay and minimal power overhead, is the most efficient one. Here, we propose a novel algorithm that outperforms the state-of-the-art design by sixfold in terms of speed, without sacrificing the accuracy for a real-time, hand movement intention detection based on the adaptive wavelet transform with only 1 s detection delay and maximum sensitivity of 88% and selectivity of 78% (only 7% loss of sensitivity).

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