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Efficient hardware implementation of real-time low-power movement intention detector system using fft and adaptive wavelet transform

Chamanzar, A ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1109/TBCAS.2017.2669911
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2017
  4. Abstract:
  5. 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). © 2016 IEEE
  6. Keywords:
  7. Adaptive wavelet ; BCI ; Continuous wavelet transform (CWT) ; EEG ; FFT ; Movement intention ; Selectivity ; Sensitivity ; Brain computer interface ; Catalyst selectivity ; Electroencephalography ; Fast Fourier transforms ; Hardware ; Adaptive wavelet transforms ; Adaptive wavelets ; Brain-computer interfacing ; Continuous wavelet transforms ; Electroencephalogram signals ; Hardware implementations ; Movement intentions ; Wavelet transforms ; Algorithm ; Behavior ; Human ; Movement (physiology) ; Algorithms ; Brain-Computer Interfaces ; Equipment Design ; Humans ; Intention ; Movement ; Wavelet Analysis
  8. Source: IEEE Transactions on Biomedical Circuits and Systems ; Volume 11, Issue 3 , 2017 , Pages 585-596 ; 19324545 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/7930496