Abstract EEG-based emotion recognition has attracted substantial attention from researchers due to its extensive application prospects, and progress been made in feature extraction classification modelling EEG data. However, insufficient high-quality training data are available for building models via machine learning or deep methods. The artificial generation of is an effective approach overco...

We introduce and compare several strategies for learning discriminative features from electroencephalography (EEG) recordings using deep learning techniques. EEG data are generally only available in small quantities, they are highdimensional with a poor signal-to-noise ratio, and there is considerable variability between individual subjects and recording sessions. Our proposed techniques specif...

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a f...

Adaptive decision making depends on the accurate representation of rewards associated with potential choices. These representations can be acquired with reinforcement learning (RL) mechanisms, which use the prediction error (PE, the difference between expected and received rewards) as a learning signal to update reward expectations. While EEG experiments have highlighted the role of feedback-re...

In spite of the high prevalence of epilepsy and the importance of preserving cognitive function in people with learning disabilities, this population has received relatively little research attention. This study sets out systematically to investigate possible predictive factors of inter-ictal states of arousal and attention. The daytime function of 28 people with epilepsy and severe learning di...

Automatic emotion recognition is one of the most challenging tasks. To detect emotion from nonstationary EEG signals, a sophisticated learning algorithm that can represent high-level abstraction is required. This study proposes the utilization of a deep learning network (DLN) to discover unknown feature correlation between input signals that is crucial for the learning task. The DLN is implemen...

Diagnosis of epilepsy can be expensive, time-consuming, and often inaccurate. The gold standard diagnostic monitoring is continuous video-electroencephalography (EEG), which ideally captures all epileptic events dis-charges. Automated seizures activity from EEG would save time resources, it the focus much EEG-based research. purpose this paper to provide a survey in order understand, classify b...

With advancements in technology, video EEG (VEEG) data can be recorded utilizing higher sampling rate options to capture higher frequencies that have been associated with seizures. Video recording options that include High Definition (HD) settings for a clearer view of subtle clinical seizure changes are also available. With all these advantages, it is essential to develop an infrastructure tha...

A statistical method for finding the optimal preictal period to be used in epileptic seizure prediction algorithms is presented. As supervised machine learning methods need labeled training samples, the adequate selection of preictal period plays a key role in the training of an efficient classifier employed in seizure prediction. The proposed method uses amplitude distribution histograms of a ...

Background and aims: Paper-pencil tests have always its own problems in the mental disorders evaluation, including learning questions, bad or good blazon are the problems with this methodology. This study aimed to propose a new alternative method of measuring mental disorders without paper-pencil test using EEG. Methods: The research society involved depr...