Document Type : Full Research Paper


1 Department of Biomedical Engineering, AmirKabir University of Technology Research Center of Intelligent Signal Processing

2 Department of Biomedical Engineering, AmirKabir University of Technology

3 Research Center of Intelligent Signal Processing Department of Biomedical Engineering, Azad University of Mashhad



P300 is the most predominant cognitive component of the brain signals. In this study, the single trial event related potentials recorded from the scalp, were decomposed to their time-frequency components using discrete wavelet transform. These quantities were later analyzed as the features related to the cognitive activities of brain. Study on these features showed that cognitive processes of the brain of ten reflected in the feature of δ and θ bands. The aim of this study, as a primary step for "lie detection using brain signals (EEG - Polygraphy)", was to design a system for discriminating between single trials involved P300 and those without it. In the first approach, an optimal discriminant function based on 9 features was designed using "Stepwise Linear Discriminant Analysis". Detection accuracy was 75% in training data and 71% in test data. More study on this method showed that almost similar accuracy could be obtained from the features of Pz channel alone. In the second approach, the modular learning strategy - based on principal component analysis and neural networks - was used. After training the systems, the maximum classification accuracy was 76% in train data and 72% in test data.


Main Subjects

[1]     Niedermeyer E, Lopes Da, Silva F; Electoencephalography; USA; Williams and Wilkins; 2000:637-655 & 1073-1091.
[2]     Basar E, Schurmann M, Demiralp T, Basar-Eroglu C, Ademoglu A; Event-related oscillations are ‘real brain responses’- wavelet analysis and new strategies; International Journal of Psychophysiology 2001; 39:91- 127.
[3]     Basar E, Basar-Eroglu C, Karakas S, Schurmann M; Gamma, alpha, delta, and theta oscillations govern cognitive processes; International Journal of Psychophysiology 2001; 39:241-248.
[4]     Basar E, Basar_Eroglu C, Karakas S, Schurmann M; Are cognitive processes manifested in event-related gamma, alpha, theta and delta oscillation in the EEG?; Neuroscience Letters 1999; 259:165-168.
[5]     Farwell LA; Method and apparatus for truth detection; United State Patent; 1995; Patent Number: 5,406,956.
[6]     Miller AR, Baratta C, Weynveen C, Rosenfeld JP; False memory: P300 amplitude, topography and latency; Psychology Department; Northwestern University; USA;1999; ( rosenfeld/publications.html).
[7]     Polikar R, Greer MH, Upda L, Keinert F; Multiresolution wavelet analysis of ERPs for the detection of Alzheimer’s disease; Proceedings of 19th International Conference of IEEE/EMBS; Chicago; IL; USA; 1997:1301-1304.
[8]     Polikoff JB, Bunnell HT, Borkowski WJ; Toward a P300-based computer interface; Applied Science and Engineering Laboratories; A. I. duPont Institute; Wilmington; ( )
[9]     Bayliss JD; A flexible brain-computer interface; PhD. Thesis; University of Rochester; Rochester; NewYork; 2001.
[10] Rosenfeld JP; Event-related potentials in detection of deception; Psychology Department; Northwestern University; Evanston; IL 60208, USA; 1999; (
[11] Farwell LA, Donchin E; The truth will out: interrogative polygraphy (‘lie detection’) with event-related brain potentials; Psychophysiology 1991; 28(5):531-547.
[12] Allen JJ, Iaccono WG; A comparison of methods for analysis of event-related potentials in deception detection; Psychophysiology 1997; 34:234-240.
[14] ابوطالبی وحید؛ تجزیه و تحلیل مولفه‌های شناختی سیگنال الکتریکی مغز و کاربرد آن در دروغ‌سنجی؛ پیشنهاد رساله دکترای مهندسی پزشکی-بیوالکتریک؛ دانشکده مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، اردیبهشت‌ماه 1382.
[15] Jansen BH, Allam A, Kota P, Lachance K, Oscho A, Sundarsan K; An exploratory study of factors affecting single trial P300 detection; IEEE Transactions on Biomedical Engineering 2004; 51(6):975-978.
[16] Quiroga RQ; Quantitative analysis of EEG signals: timefrequency methods and chaos theory; PhD. Thesis; Medical University of Lübeck; Germany; 1998.
[17] Quiroga RQ, Sakowitz OW, Basar E, Schurmann M; Wavelet transform in the analysis of frequency composition of evoked potentials; Brain Research Protocols 2001; 8:16-24.
[18] Ademoglu A, Micheli_Tzanakou E, Istefanopulos Y; Analysis of pattern reversal visual evoked potentials (PRVEP’s) by spline wavelets; IEEE Transactions on Biomedical Engineering 1997; 44(9):881-890.
[19] Demiralp T, Istefanopulos Y, Ademoglu A, Yordanova J, Kolev V; Analysis of functional component of P300 by wavelet transform; Proceedings of 20th International Conference of IEEE/EMBS 1998; 20(4):1992-1995.
[20] Haykin S, Thomson DJ; Signal detection in a nonstationary environment reformulated as an adaptive pattern classification problem; Proceedings of IEEE 1998; 86(11).
[21] Fukunaga J; Statistical pattern recognition; 2nd ed. New York; Academic Press; 1990.
[22] Yu HH, Hwang JN; Handbook of neural network signal processing; CRC Press; 2002.
[23] Demiralp T, Ademoglu A, Istefanopulos Y, Basar-Eroglu C, Basar E; Wavelet analysis of oddball P300; International Journal of Psychophysiology 2001; 39:221- 227.
[24] Quiroga RQ, Garcia H; Single-trial event-related potentials with wavelet denoising; Clinical Neurophysiology 2003; 114:376-390.
[25] عبدالله زاده میلانی علی؛ آنالیز پتانسیل‌های وابسته به رخداد در یک تک‌آزمایش چندکاناله EEG با استفاده از آنالیز مولفه‌های مستقل؛ پایان‌نامه کارشناسی ارشد مهندسی پزشکی-بیوالکتریک؛ دانشکده مهندسی پزشکی، دانشگاه صنعتی امیرکبیر، اردیبهشت‌ماه 1383.
[26] Xu N, Gao X, Hong B, Miao X, Gao S, Yang F; BCI competition 2003-data set IIb:enhancing P300 wave detection using ICA based subspace projections for BCI applications; IEEE Transactions on Biomedical Engineering 2004; 51(6):1067-1072.