EEG is a record of brain activity from various sites of the brain and artefacts are unwanted noise signals in an EEG record. Classification of artefacts is based on the source of generation like physiological artefacts and external artefacts. The body of the subjects is the main source of Physiological artefacts, while external artefacts are from outside the body due to the environment and measurement device. Recognition, identification and elimination of artefacts is an important process to minimize the chance of misinterpretation of EEG. Clinical and non-clinical fields such as brain computer interface, intelligent control system robotics etc.all require removal of artefacts. Artefacts can be removed very easily using manual and filtering methods because of their morphology and electrical characteristic. Electro Oculogram (EOG) artefact using manual and filter method is very difficult to remove. Artefact removing algorithms are the most suited techniques for EOG artefact removal.
Introduction: Electroencephalography (EEG) has been used extensively to study affective disorders. Quantitative spectral analysis of an EEG scan has been used to assess the biological basis of emotional disorders such as depression as well as to investigate biomarkers of affective disorders. Inter-hemispheric asymmetries in both baseline and stimulus-evoked frequencies (alpha, beta, theta, and delta) are potential biomarkers of depression. The role of frontal alpha asymmetry has been established, but other spectral frequencies such as frontal theta remain elusive. We compared the hemispheric differences in frontal theta power in depressed patients and controls before and during listening to music to study the correlation of frontal theta asymmetry with depression. Methods: To determine whether stimulus-evoked frontal theta asymmetry is a biomarker of depression, we compared 23 patients with mild depression (based on the Hamilton Depression Rating Scale) with 17 age-and sex-matched controls by conducting EEG at rest and after listening to Indian classical music. Results: In controls without depression, the mean frontal theta power of the left hemisphere and frontal theta asymmetry increased significantly during music listening. In depressed patients, frontal theta asymmetry was reversed during music listening. Conclusion: Frontal theta asymmetry is a potential biomarker of depression.
Background: Depression, despite being the most common of mental illness lacks any quantifiable and absolute biomarker. Frontal alpha asymmetry (FAA) is proposed as biomarker of depression both in resting and activated state. Yet, the location of extraction of alpha, clinical utility as well as validity of FAA is uncertain. With aim of obtaining clarity on this confusion we conducted this study. Methodology: Electroencephalographic frontal alpha power was calculated in patients of depression (n = 24) and compared with healthy controls (n = 17) for the assessment of FAA. Both groups were studied for resting phase and activation phase changes in FAA. For activation phase, auditory stimuli in the form of Indian classical music were used. Results: Frontal alpha power was measured across FP1, FP2, F3, F4, F7, and F8. Mean powers were compared in resting (before), activated (during) and postactivated resting stage (after). FAA was statistically significant in F7–F8 pair of electrodes and on F7 electrode when compared between cases and controls. Conclusion: Quest for biomarker for depression churned out FAA as frontrunner. Despite of vast amount of research on it, practical utility eludes us. We need to revisit our approach from conventional search of the diagnostic biomarker; as FAA might reflect component of depression but not totally disorder. In our opinion, we are not yet ready for it and have a road ahead to travel.
This literature survey attempts to clarify different approaches considered to study the impact of the musical stimulus on the human brain using EEG Modality. Glancing at the field through various aspects of such studies specifically an experimental protocol, the EEG machine, number of channels investigated, feature extracted, categories of emotions, the brain area, the brainwaves, statistical tests, machine learning algorithms used for classification and validation of the developed model. This article comments on how these different approaches have particular weaknesses and strengths. Ultimately, this review concludes a suitable method to study the impact of the musical stimulus on brain and implications of such kind of studies.
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