Automatic recognition of chewing noises in epileptic EEG based on period segmentation

Acharya, 2013, Automated EEG analysis of epilepsy, Knowl-Based Syst., 45, 147, 10.1016/j.knosys.2013.02.014

Gotman, 1976, Automatic recognition and quantification of interictal epileptic activity in the human scalp EEG, Electroencephalogr. Clin. Neurophysiol., 41, 513, 10.1016/0013-4694(76)90063-8

Dhiman, 2014, Genetic algorithms tuned expert model for detection of epilepticseizures from EEG signatures, Appl. Soft. Comput., 19, 8, 10.1016/j.asoc.2014.01.029

Kumar, 2014, Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine, Neurocomputing, 133, 271, 10.1016/j.neucom.2013.11.009

Sharma, 2015, Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions, Expert. Syst. Appl., 42, 1106, 10.1016/j.eswa.2014.08.030

Zhang, 2013, L1-regularized multiway canonical correlation analysis for SSVEP-based BCI, IEEE Trans. Neural Syst. Rehabil. Eng., 21, 887, 10.1109/TNSRE.2013.2279680

Jin, 2014, An ERP-based BCI using an oddball paradigm with different faces and reduced errors in critical function, Int. J. Neural Syst., 24, 1450027, 10.1142/S0129065714500270

Wu, 2015, Probabilistic common spatial patterns for multichannel EEG analysis, IEEE Trans. Pattern Anal. Mach. Intell., 37, 639, 10.1109/TPAMI.2014.2330598

Gotman, 1982, Automatic recognition of epileptic seizures in the EEG, Electroencephalogr. Clin. Neurophysiol., 54, 530, 10.1016/0013-4694(82)90038-4

Feinberg, 1978, Period and amplitude analysis of 0.5–3c/sec activity in NREM sleep of young adults, Electroencephalogr. Clin. Neurophysiol., 44, 202, 10.1016/0013-4694(78)90266-3

Zhang, 2013, Automatic detection of interictal epileptiform discharges based on time-series sequence merging method, Neurocomputing, 110, 35, 10.1016/j.neucom.2012.11.017

Khademul Islam Molla, 2012, Artifact suppression from EEG signals using data adaptive time domain filtering, Neurocomputing, 97, 297, 10.1016/j.neucom.2012.05.009

Jafarifarmand, 2013, Artifacts removal in EEG signal using a new neural network enhanced adaptive filter, Neurocomputing, 103, 222, 10.1016/j.neucom.2012.09.024

Hu, 2015, Removal of EOG and EMG artifacts from EEG using combination of functional link neural network and adaptive neural fuzzy inference system, Neurocomputing, 151, 278, 10.1016/j.neucom.2014.09.040

Yagyu, 1998, Smell and taste of chewing gum affect frequency domain EEG source localizations, Int. J. Neurosci., 93, 205, 10.3109/00207459808986426

Gotman, 1990, Automatic seizure detection, Electroencephalogr. Clin. Neurophysiol., 76, 317, 10.1016/0013-4694(90)90032-F

Fujimori, 1958, Analysis of electroencephalogram of children by histogram method, Electroenceph. Clin. Neurophysiol., 10, 241, 10.1016/0013-4694(58)90031-2

Weidong Zhou, J. Gotman, Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA, in: Conference Proceedings. IEEE Engineering in Medicine and Biology Society, vol. 1, 2004, pp. 392–395.

Uchida, 1999, A comparison of period amplitude analysis and FFT power spectral analysis of all-night human sleep EEG, Physiol. Behav., 67, 121, 10.1016/S0031-9384(99)00049-9

Yamamoto, 1975, Automatic EEG diagnosing system-with special regard to the waveform recognition method, Psychiat. Neurol. Jpn., 77, 127

Zhang, 2014, Aggregation of sparse linear discriminant analyses for event-related potential classification in brain-computer interface, Int. J. Neural Syst., 24, 1450003, 10.1142/S0129065714500038

Nguyen, 2012, EOG artifact removal using a wavelet neural network, Neurocomputing, 97, 374, 10.1016/j.neucom.2012.04.016