Novel Hybrid Bioelectrodes for Ambulatory Zero-Prep EEG Measurements Using Multi-channel Wireless EEG System
This paper describes a wireless multi-channel system for zero-prep electroencephalogram (EEG) measurements in operational settings. The EEG sensors are based upon a novel hybrid (capacitive/resistive) bioelectrode technology that requires no modification to the skin’s outer layer. High impedance techniques developed for QUASAR’s capacitive electrocardiogram (ECG) sensors minimize the sensor’s susceptibility to common-mode (CM) interference, and permit EEG measurements with electrode-subject impedances as large as 107 Ω. Results for a side-by-side comparison between the hybrid sensors and conventional wet electrodes for EEG measurements are presented. A high level of correlation between the two electrode technologies (>99 subjects seated) was observed. The electronics package for the EEG system is based upon a miniature, ultra-low power microprocessor-controlled data acquisition system and a miniaturized wireless transceiver that can operate in excess of 72 hours from two AAA batteries.
KeywordsEEG biosensors high impedance wireless
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- 1.Matthews, R., McDonald, N.J., Trejo, L.J.: Psycho-Physiological Sensor Techniques: An Overview. In: 11th International Conference on Human Computer Interaction (HCII), pp. 22–27. Las Vegas, NV (July 2005)Google Scholar
- 3.Sullivan IV, J.J.: Fighting Fatigue. Public Roads 67, 18–23 (2003)Google Scholar
- 4.Wolpaw, J.R., McFarland, D.J.: An EEG-based brain-computer interface for cursor control. Electroenceph. Clin. Neruphysiol. 78, 252–259 (1991)Google Scholar
- 6.Matthews, R., McDonald, N.J., Fridman, I., Hervieux, P., Nielsen, T.: The invisible electrode - zero prep time, ultra low capacitive sensing. In: 11th International Conference on Human Computer Interaction (HCII), pp. 22–27. Las Vegas, NV (July 2005)Google Scholar
- 7.Jasper, H.H.: The ten-twenty electrode system of the international federation. Electroencephalogr. Clin. Neurophysiol. 10, 371–375 (1958)Google Scholar
- 10.Wallerius, J., Trejo, L.J., Matthews, R., Rosipal, R., Caldwell, J.A.: Robust feature extraction and classification of EEG spectra for real-time classification of cognitive state. In: 11th International Conference on Human Computer Interaction (HCII), pp. 22–27. Las Vegas, NV (July 2005)Google Scholar