A new approach for EEG feature extraction in P300-based lie detection

Abootalebi, Vahid; Moradi, Mohammad Hassan; Khalilzadeh, Mohammad A.

doi:10.1016/j.cmpb.2008.10.001

Cited by 201 publications

(136 citation statements)

References 19 publications

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“…These three channels are the channels used in previous P300 component-based lie detection research [6]. During the recording experiment, allocated time for each subject is one minute for five questions.…”

Section: Eeg Signal Recordingmentioning

confidence: 99%

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A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection

Caesarendra

2017

JEMMME

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Section: Eeg Signal Recordingmentioning

confidence: 99%

“…In this study, the EEG rough signal with a sampling frequency of 500 Hz will be filtered with a frequency range of 0.3-30 Hz; The frequency range is commonly used for ERP analysis [6]. The filtering process is done by entering the data encoding for the band pass filter in the MATLAB command line.…”

Section: Data Processingmentioning

confidence: 99%

A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection

Caesarendra

2017

JEMMME

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“…[3] Finally, neurophysiology-based lie detection, assessing neurophysiological orientation responses (P300) to compromising stimuli, has been shown to be feasible in specific situations. [7,20] 2 The second variety of stimulus-dependent passive BCIs includes affective tagging of media and communication of preference. Affective tagging uses affective responses observed to media, such as songs, music videos, or films.…”

Section: Brain-computer Interfaces 67mentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

238

159

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Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with the goal of enhancing human-computer interaction with an affective component. With the advent of brain-computer interface research, the idea of affective brain-computer interfaces (aBCI), enabling affect detection from brain signals, arose. In this article, we set out to survey the field of neurophysiology-based affect detection. We outline possible applications of aBCI in a general taxonomy of brain-computer interface approaches and introduce the core concepts of affect and their neurophysiological fundamentals. We show that there is a growing body of literature that evidences the capabilities, but also the limitations and challenges of affect detection from neurophysiological activity

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“…The subjects need not to be trained to induce P300 potential and the time of single experiment can be very short due to the delay of the P300 is very short. But, the information conversion speed of P300-BCI is not high enough to realize real-time control [2]. (2) Event-Related Synchronizations or Desynchronizations (ERS/ERD).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Feature Extraction Algorithms Used in Brain-Computer Interfaces

Tan¹,

Zhao²,

Sun³

et al. 2017

dtetr

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Abstract. A brain-computer interface (BCI) is a system for communication between humans or animals and computers, which sends messages or commands from brain activities to the external devices without peripheral nerves and muscles activities. Feature extraction is crucial in a BCI system, for it determines whether the user's intent can be interpreted as an accurate command. We examined the performance of the representative algorithms including Fast Fourier transform (FFT), Wavelet transform (WT) and Independent component analysis (ICA). Experimental results show that these algorithms are effective to identify the target characteristics. Improvements of these methods or their integrations can contribute to enhance the efficiency of the BCI systems.

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A new approach for EEG feature extraction in P300-based lie detection

Cited by 201 publications

References 19 publications

A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection

A Method to Extract P300 EEG Signal Feature Using Independent Component Analysis (ICA) for Lie Detection

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Analysis of Feature Extraction Algorithms Used in Brain-Computer Interfaces

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