Informatics Publishing Limited

M. Suresh ¹, P. G. Krishnamohan ², Mallikarjun S. Holi ³

Affiliations
1 Department of ECE, Kalpataru Institute of Technology, Tiptur, IN
2 Department of ECE, JNTU College of Engineering, Kukatpally, Hyderabad, IN
3 Department of Biomedical Engineering, Bapuji College of Engineering & Tech, Davangere, IN

Abstract

The identification of a person using biometric data may be of interest. A direct connection between muscle, central nervous system and brain is unique to an individual. Here we have demonstrated the significance of electromyogram (EMG) signal for person identification using cepstral and non uniform filter bank features. Our approach consists of a robust feature extraction scheme which is based on cepstral analysis and non uniform filter bank with a specified configuration, combined with vector quantization modelling. Various experiments have been conducted to determine the person identification performance in our proposed scheme. Preliminary results indicate that with well-chosen feature extraction, an identification rate of up to 93.8766% is achievable for a database consisting of forty nine individuals, with collected 3 sessions EMG data in a gap of one day duration.

Keywords

Electromyogram (EMG), Biometrics, Verification, Identification, Vector Quantization.

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Sandhya Dass ¹, Mallikarjun S. Holi ², Soundararajan ³

Affiliations
1 Department of E&IE, Dayananda Sagar College of Engg, Bengaluru, 560078, IN
2 Department of E&IE, UBDT College of Engg, Davangere,577004, IN
3 Teegala Krishna Reddy Engineering College, Hyderabad-500097, IN

Abstract

Brainstem auditory evoked potentials (BAEP) are electrical potentials recorded in response to an auditory stimulus. Wavelet transform is adopted to extract the characteristic features of BAEP for interpretation and assessment. The results shows that there is significant difference (p<0.05) in the wavelet coefficients features in normal and abnormal BAEPs.

Keywords

Brainstem Auditory Evoked Potentials, Discrete Wavelet Transform, Wavelet Transform.

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