Oriental Journal of Computer Science and Technology

Ali Yadollahpour ¹, Abdolhossein Bagdeli ¹

Affiliations
1 Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Golestan Blvd., Ahvaz, IR

Abstract

Brain computer interface (BCI) is relatively new technology aiming at assisting, augmenting, or repairing human cognitive or sensory-motor functions. Recent advances in bio-signal processing as well as advances in neuro-imaging techniques have boosted BCI development. The most important barrier in developing BCI technology is currently the lack of a sensor modality that provides safe, accurate and robust access to brain signals. However, recent advances in biosensor technology, signal processing and new insights into association between EEG-based measures and mental states and advances in high resolution EEG measurements have dramatically revolutionized the BCI development. The present study reviews the basic principles of BCI, recent advances and future directions of the technology.

Keywords

Brain Computer Interface (BCI), EEG, Motor Functions.

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Ali Yadollahpour ¹, Mahmud Naraqi Arani ¹

Affiliations
1 Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IR

Abstract

Depression is one of the most common and debilitating diseases worldwide, especially in industrialized countries. Depression is traditionally managed with psychological, pharmacological, or physical interventions alone or in combination forms. However, all of the conventional methods have various limitations, such as medication side effects, frequent relapse, and high economics costs. In addition, a significant portion of the depressive disorders are treatment-refractory that do not respond to the usual medications. These different challenges emphasize the need for effective alternative treatment for depression. Neurofeedback technique (NFT) is a noninvasive and non-drug treatment through which patients learn to modulate their brain activities using repeated practice to eliminate the disease-specific patterns in electroencephalogram (EEG) of the patient. NFT emphasizes on the correlation between EEG and cognitive and behavioral disorders. There are several clinical protocols for NFT in treatment of Depression. All of these protocols aim to shift the brain activities, EEG waves, from disorder into normal state. Therefore, in developing efficient NFT for depression or any other disorders, determining the EEG based indices which are specifically correlated with different cognitive states is necessary. Several protocols have been developed based on these indices for depressive disorders and some of them are clinically used. Left to the right hemisphere alpha predominance, decreasing Theta/Beta ratio in left prefrontal cortex, decreasing left- or increasing right- hemispheric alpha activity, shifting an asymmetry index toward the right to rebalance activation levels in favor of the left hemisphere are some of the main protocols. This paper reviews the basic principles of NFT, its procedures and protocols for the treatment of depressive disorders and clinical outcomes.

Keywords

Neurofeedback, Biofeedback, Depression, Electroencephalography, Quantitative Electroencephalography, Treatment.

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