Brain Waves Characteristics in Individuals with Obsessive-Compulsive Disorder: A Preliminary Study

Abstract

Obsessive-compulsive disorder (OCD) is a mental illness causing patients to suffer from recurring undesirable thoughts (obsessions) conducting to do affairs repetitively (compulsions). Brain signals recorded by Electroencephalogram (EEG) can be analyzed in order to present a diagnostic procedure considering the localization approach. In this study, the signals acquired by EEG have been recorded from three groups; two case groups; patients with severe obsessive symptoms and patients with severe compulsive symptoms, and one healthy control group. Brain signal processing techniques have been applied on the signals emitted from frontal and parieto-occipital regions to discover the features leading to the best discrimination between case groups and healthy controls. In this regard, after preprocessing, the features of time and frequency domains presenting the significant meaningful relation were nominated for classification by linear discrimination analysis (LDA). Although the parieto-occipital region performed better in the diagnosis for both obsessive and compulsive groups, the features gained from the frontal cortex resulted in better discrimination for only the compulsive group. In addition, time domain features had a more significant influence in diagnosis rather than frequency domain for both case groups. The study presented particular characteristics of brain signals in two dimensions of OCD in specific brain regions leading to more accurate presurgical assessments in the studies between the affected brain regions and behavioral issues.

LabVIEW Instrument Aimed for the Research on Brain-Computer Interface by Enabling the Acquisition, Processing, and the Neural Networks based Classification of the Raw EEG Signal Detected by the Embedded NeuroSky Biosensor

“…Nevertheless, the future version of the presented virtual instrument related to BCI research should be updated to handle more EEG channels to classify complex mental activities efficiently or even manage hybrid BCI systems [15][16][17]. Moreover, the aim is to use, as much as possible, the most inexpensive portable EEG headsets, for example, Muse and Emotiv Insight, to ensure the simple-to-use working principle and availability for the researchers with minimal experience related to the Brain-Computer Interface field.…”

Machine Learning Approaches for Obsessive Compulsive Disorder Detection

“…The limitation of this study is they only use the neuroimaging biomarkers, not the Oxidative stress biomarkers. In [24] the author discusses the obsessions, compulsions, and healthy control of the patient by using the EEG signal. The EEG signal is recorded from the severe obsession patient, severe compulsions, and healthy control patient.…”

Brain Dynamics in Response to Intermittent Photic Stimulation in Epilepsy

“…Figure 1 shows a general overview of brain signal processing steps. Research shows that brain emits specific range of frequency bandwidths in certain times such as Delta (0.5-4 Hz); during deep and dreamless sleep, Theta (4-8 Hz); during relaxation and meditation, Alpha (8)(9)(10)(11)(12); in awake and calm-ness, Beta (12-30 Hz); lower beta where the subject is awake and higher beta confronting anxious, and Gamma (over 30 Hz); during hyper brain activity [20], [21]. Figure 2 presents various frequency bandwidths of the brain signals.…”