Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

Abstract

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”

Адаптивная Фильтрация Физиологических Артефактов На Сигналах Электроэнцефалограмм Человека С Использованием Разложения По Эмпирическим Модам

“…Одним из наиболее простых и часто используемых является метод на основе визуального поиска артефактов [10,11]. Ме-тод предполагает визуальный (или полуавтоматический) анализ временных реализаций ЭЭГ и поиск артефактов опытным нейрофизиологом.…”

Resting cranial and upper cervical muscle activity is increased in patients with migraine

“…The cap included electrodes over muscles such as the frontalis, orbicularis and temporalis, and also close to nuchal (upper cervical) muscles. At frequencies above 10-20 Hz, EMG signals are the largest contributors to scalp electrical recordings, with the highest power typically in the range 60-90 Hz (Goncharova et al , 2003, Whitham et al , 2007, Whitham et al , 2008, Zhang et al , 2015. Recent research indicates that this is true even when the subject is relaxed and phasic muscle has been excised from the recordings (Whitham et al , 2007, Whitham et al , 2008, Pope et al , 2009, Yilmaz et al , 2014 .…”