AIMC Topic: Electroencephalography

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Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine.

The Review of scientific instruments
In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automobiles, entertainment, and so on. Motor imagery relevant electroencephalogram (EEG) signals are weak, ...

A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold.

International journal of neural systems
Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-va...

Robot-Assisted Insular Depth Electrode Implantation Through Oblique Trajectories: 3-Dimensional Anatomical Nuances, Technique, Accuracy, and Safety.

Operative neurosurgery (Hagerstown, Md.)
BACKGROUND: The insula is a deep cortical structure that has renewed interest in epilepsy investigation. Invasive EEG recordings of this region have been challenging. Robot-assisted stereotactic electroencephalography has improved feasibility and saf...

Electroencephalogram-Based Emotion Recognition Using a Particle Swarm Optimization-Derived Support Vector Machine Classifier.

Critical reviews in biomedical engineering
We sort human emotions using Russell's circumplex model of emotion by classifying electroencephalogram (EEG) signals from 25 subjects into four discrete states, namely, happy, sad, angry, and relaxed. After acquiring signals, we use a standard databa...

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

Bio-medical materials and engineering
BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an importa...

Classification of Alzheimer's Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation.

Journal of Alzheimer's disease : JAD
BACKGROUND: Several studies investigated clinical and instrumental differences to make diagnosis of dementia in general and in Alzheimer's disease (AD) in particular with the aim to classify, at the individual level, AD patients and healthy controls ...

Assessment of mental workload based on multi-physiological signals.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Mental workload is one of the contributing factors to human errors in road accidents or other potentially adverse incidents.

General principles of machine learning for brain-computer interfacing.

Handbook of clinical neurology
Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair,...

Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation.

JAMA neurology
IMPORTANCE: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline. However, there is a scarcity of experts qualified to interpret EEG results. Prior attempts to autom...

Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder.

Cerebral cortex (New York, N.Y. : 1991)
Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variabi...