Multi-Class Seizure Type Classification Using Features Extracted from the EEG.

In this study, we classify the seizure types using feature extraction and machine learning algorithms. Initially, we pre-processed the electroencephalogram (EEG) of focal non-specific seizure (FNSZ), generalized seizure (GNSZ), tonic-clonic seizure (TCSZ), complex partial seizure (CPSZ) and absence seizure (ABSZ). Further, 21 features from time (9) and frequency (12) domain were computed from the EEG signals of different seizure types.

Optimization of Pre-Ictal Interval Time Period for Epileptic Seizure Prediction Using Temporal and Frequency Features.

Epilepsy is a neurological disorder characterized by recurrent seizures. Automated prediction of epileptic seizures is essential in monitoring the health of an epileptic individual to avoid cognitive problems, accidental injuries, and even fatality. In this study, scalp electroencephalogram (EEG) recordings of epileptic individuals were used to predict seizures using a configurable Extreme Gradient Boosting (XGBoost) machine learning algorithm.

Emotion Recognition from Spatio-Temporal Representation of EEG Signals via 3D-CNN with Ensemble Learning Techniques.

The recognition of emotions is one of the most challenging issues in human-computer interaction (HCI). EEG signals are widely adopted as a method for recognizing emotions because of their ease of acquisition, mobility, and convenience. Deep neural networks (DNN) have provided excellent results in emotion recognition studies.

Comprehensive Analysis of Feature Extraction Methods for Emotion Recognition from Multichannel EEG Recordings.

Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. However, most studies in this area have used relatively small monocentric data and focused on a limited range of EEG features, making it difficult to compare the utility of different sets of EEG features for emotion recognition. This study addressed that by comparing the classification accuracy (performance) of a comprehensive range of EEG feature sets for identifying emotional states, in terms of valence and arousal.

Assessing attentive monitoring levels in dynamic environments through visual neuro-assisted approach.

Objective: This work aims to establish a framework in measuring the various attentional levels of the human operator in a real-time animated environment through a visual neuro-assisted approach.

Background: With the increasing trend of automation and remote operations, understanding human-machine interaction in dynamic environments can greatly aid to improve performance, promote operational efficiency and safety.

Method: Two independent 1-hour experiments were conducted on twenty participants where eye-tracking metrics and neuro activities from electroencephalogram (EEG) were recorded.

EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning.

Electroencephalogram (EEG) based emotion classification reflects the actual and intrinsic emotional state, resulting in more reliable, natural, and meaningful human-computer interaction with applications in entertainment consumption behavior, interactive brain-computer interface, and monitoring of psychological health of patients in the domain of e-healthcare. Challenges of EEG-based emotion recognition in real-world applications are variations among experimental settings and cognitive health conditions. Parkinson's Disease (PD) is the second most common neurodegenerative disorder, resulting in impaired recognition and expression of emotions.

Time-Frequency Decomposition of Scalp Electroencephalograms Improves Deep Learning-Based Epilepsy Diagnosis.

Epilepsy diagnosis based on Interictal Epileptiform Discharges (IEDs) in scalp electroencephalograms (EEGs) is laborious and often subjective. Therefore, it is necessary to build an effective IED detector and an automatic method to classify IED-free versus IED EEGs. In this study, we evaluate features that may provide reliable IED detection and EEG classification.

Automated EEG pathology detection based on different convolutional neural network models: Deep learning approach.

The brain electrical activity, recorded and materialized as electroencephalogram (EEG) signals, is known to be very useful in the diagnosis of brain-related pathology. However, manual examination of these EEG signals has various limitations, including time-consuming inspections, the need for highly trained neurologists, and the subjectiveness of the evaluation. Thus, an automated EEG pathology detection system would be helpful to assist neurologists to enhance the treatment procedure by making a quicker diagnosis and reducing error due to the human element.

Automated Adult Epilepsy Diagnostic Tool Based on Interictal Scalp Electroencephalogram Characteristics: A Six-Center Study.

The diagnosis of epilepsy often relies on a reading of routine scalp electroencephalograms (EEGs). Since seizures are highly unlikely to be detected in a routine scalp EEG, the primary diagnosis depends heavily on the visual evaluation of Interictal Epileptiform Discharges (IEDs). This process is tedious, expert-centered, and delays the treatment plan.

An Emotion Assessment of Stroke Patients by Using Bispectrum Features of EEG Signals.

Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states.

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing.

Objective: While Parkinson's disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing.

Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls.

Review of emotion recognition in stroke patients.

Objective: Patients suffering from stroke have a diminished ability to recognize emotions. This paper presents a review of neuropsychological studies that investigated the basic emotion processing deficits involved in individuals with interhemispheric brain (right, left) damage and normal controls, including processing mode (perception) and communication channels (facial, prosodic-intonational, lexical-verbal).

Methods: An electronic search was conducted using specific keywords for studies investigating emotion recognition in brain damage patients.