Title :
Estimating the hurst exponent in motor imagery-based brain computer interface
Author :
Aldea, Roxana ; Tarniceriu, Daniela
Author_Institution :
Telecommun. & Inf. Technol., “Gheorghe Asachi” Tech. Univ. of Iasi, Iasi, Romania
Abstract :
The objective of this paper is to detect sensorimotor rhythms (mu and beta) produced by right and left hand motor imagery. The electroencephalographic (EEG) data were recorded with 8 g.tec active electrodes by means of g.MOBIlab+ module. The EEG data are wavelet multiresolution decomposed into subbands of interest (7.5-15 Hz-mu rhythm, 15-30Hz-beta rhythm). We applied absolute moment and aggregated variance methods to estimate the Hurst exponent of these decomposed signals, with different types of wavelet. We obtained very good discrimination on channels C3 and CP3 for right hand motor imagery signal and on channels C4 and CP4, when left hand was imaginarily moved. The subjects discriminated better the beta rhythm.
Keywords :
brain-computer interfaces; electroencephalography; medical signal processing; signal resolution; wavelet transforms; C3 channel; C4 channel; CP3 channel; CP4 channel; EEG data; Hurst exponent estimation; absolute moment; aggregated variance methods; beta rhythm detection; electroencephalographic data; g.MOBIlab+ module; g.tec active electrodes; left hand motor imagery; motor imagery-based brain computer interface; mu rhythm detection; right hand motor imagery; sensorimotor rhythm detection; signal decomposition; wavelet multiresolution analysis; Discrete wavelet transforms; Electroencephalography; Fractals; Image resolution; Method of moments; Signal resolution; Wavelet analysis; Absolute Moment Method; Aggregated Variance Method; Brain-computer interface; Hurst exponent; sensorimotor rhythms; wavelet decomposition;
Conference_Titel :
Speech Technology and Human - Computer Dialogue (SpeD), 2013 7th Conference on
Conference_Location :
Cluj-Napoca
DOI :
10.1109/SpeD.2013.6682656
