Discrete wavelet packet transform for electroencephalogram-based emotion recognition in the valence-arousal space by Ahmad, Farzana Kabir, Olakunle, Oyenuga Wasiu

“…Human emotion recognition is the key step toward innovative human-computer interactions.The advanced in computational algorithms and techniques has recently offered the promising results in recognizing human emotion.Recently, Electroencephalogram (EEG) has been shown as an effective way in identifying human emotion since it records the brain activity of human and can hardly be deceived by voluntary control.However, due to the non-linearity, non-stationary, and chaotic nature of the EEG signals, it is difficult to be examined and has been an extensive research area in the present years. …”
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Optimization of least squares support vector machine technique using genetic algorithm for electroencephalogram multi-dimensional signals by Ahmad, Farzana Kabir, Al-Qammaz, Abdullah Yousef Awwad, Yusof, Yuhanis

“…Electroencephalogram (EEG) on the other has shown to be a very effective way in recognising human emotion as this technique records the brain activity of human and they can hardly be deceived by voluntary control. …”
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Ensemble classifier for epileptic seizure detection for imperfect EEG data by Abualsaud, Khalid, Mahmuddin, Massudi, Saleh, Mohammad, Mohamed, Amr

“…Brain status information is captured by physiological electroencephalogram (EEG) signals, which are extensively used to study different brain activities.This study investigates the use of a new ensemble classifier to detect an epileptic seizure from compressed and noisy EEG signals.This noise-aware signal combination (NSC) ensemble classifier combines four classification models based on their individual performance.The main objective of the proposed classifier is to enhance the classification accuracy in the presence of noisy and incomplete information while preserving a reasonable amount of complexity.The experimental results show the effectiveness of the NSC technique, which yields higher accuracies of 90% for noiseless data compared with 85%, 85.9%, and 89.5% in other experiments.The accuracy for the proposed method is 80% when SNR = 1 dB, 84% when SNR = 5 dB, and 88% when SNR = 10 dB, while the compression ratio (CR) is 85.35% for all of the datasets mentioned.…”
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A Modified Gated Recurrent Unit Approach for Epileptic Electroencephalography Classification by Prakash, Vinod, Kumar, Dharmender

“…Electroencephalography (EEG), a non-invasive technique, records brain activities, and these recordings are routinely used for the clinical evaluation of epilepsy. …”
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