2014 International Conference on Communication and Signal Processing 2014
DOI: 10.1109/iccsp.2014.6949930
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EEG signal classification using Principal Component Analysis and Wavelet Transform with Neural Network

Abstract: The Brain-Computer Interface (BCI) is the technology that enables direct communication between the human brain and the external devices. Electroencephalography (EEG) proves to be the most studied measure of recording brain activity in BCI design. The paper is intended to analyze and extract the features of EEG signal and to classify the signal so that human emotions can be discriminated and serve as the control signal for BCI. The proposed method involves EEG data acquisition and processing which is done by fe… Show more

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Cited by 33 publications
(12 citation statements)
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“…Here, it may be described that Amplitude spectrum is the indication of amplitude information of sinusoidal within the Amplitude Spectrum signal as to the frequency axis; on the other hand, the Phase Spectrum is an indication of starting angles of sinusoidal within the signal as to the frequency axis. Because the signals in Time domain are real, Amplitude spectrums are symmetric; if a signal is symmetric in time period, its equivalent will be real in frequency domain [14, 16,17].…”
Section: Modelling By Time-amplitude and Time-frequency Analysismentioning
confidence: 99%
“…Here, it may be described that Amplitude spectrum is the indication of amplitude information of sinusoidal within the Amplitude Spectrum signal as to the frequency axis; on the other hand, the Phase Spectrum is an indication of starting angles of sinusoidal within the signal as to the frequency axis. Because the signals in Time domain are real, Amplitude spectrums are symmetric; if a signal is symmetric in time period, its equivalent will be real in frequency domain [14, 16,17].…”
Section: Modelling By Time-amplitude and Time-frequency Analysismentioning
confidence: 99%
“…El análisis de la señal con wavelets se realiza mediante la concentración de energía a través del cálculo de coeficientes, para lo cual la wavelet realiza dos procesos: filtrado de la señal utilizando un filtro pasa bajos y un filtro pasa altos de descomposición, y el submuestreo de las subseñales de salida; proceso que se repite varias veces una vez que se tenga la señal de salida del filtro pasa bajos [9].…”
Section: B Transformada Waveletunclassified
“…EEG signals are brain waves captured using various electrodes placed on human scalp. EEG signals are highly non-linear, aperiodic and time varying responses having very low frequency and small amplitude [5]. Classification of these signals relies on recognizing the region of brain depending upon the seizure pattern.…”
Section: Introductionmentioning
confidence: 99%
“…Most of the existing schemes for extracting spontaneous EEG features are based on Auto-Regressive (AR) models, Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and Wavelet Transform (WT). AR or FFT models can neither capture transient features nor the time-frequency information in a given signal [5]. STFT alleviates such time-frequency conflict by localizing both time and frequency information over uniformly spaced moving window over entire range of frequencies.…”
Section: Introductionmentioning
confidence: 99%