title: Accurate single-trial detection of movement intention made possible using adaptive wavelet transform.
abstract: The outlook of brain-computer interfacing (BCI) is very bright. The real-time, accurate detection of a motor movement task is critical in BCI systems. The poor signal-to-noise-ratio (SNR) of EEG signals and the ambiguity of noise generator sources in brain renders this task quite challenging. In this paper, we demonstrate a novel algorithm for precise detection of the onset of a motor movement through identification of event-related-desynchronization (ERD) patterns. Using an adaptive matched filter technique implemented based on an optimized continues Wavelet transform by selecting an appropriate basis, we can detect single-trial ERDs. Moreover, we use a maximum-likelihood (ML), electrooculography (EOG) artifact removal method to remove eye-related artifacts to significantly improve the detection performance. We have applied this technique to our locally recorded Emotiv(®) data set of 6 healthy subjects, where an average detection selectivity of 85 ± 6% and sensitivity of 88 ± 7.7% is achieved with a temporal precision in the range of -1250 to 367 ms in onset detections of single-trials.
authors.forename: Alireza
authors.initials: A
authors.lastname: Chamanzar
authors.forename: Alireza
authors.initials: A
authors.lastname: Malekmohammadi
authors.forename: Masih
authors.initials: M
authors.lastname: Bahrani
authors.forename: Mahdi
authors.initials: M
authors.lastname: Shabany
mesh_terms: D000465:Algorithms; D016477:Artifacts; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D004585:Electrooculography / Q000379:methods*; D006801:Humans; D033182:Intention; D009068:Movement; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis