Paper Title
Recent Computational Trends in Human Brain Signal Analysis of Electroencephalographic Data

Human brain is one of the most wondrous organs that distinguish us from all other organisms. The ability to feel, adapt, reason, remember, make decision and communicate makes human beings intelligent. Human brain is capable of processing billions of bits of information per second with the help of approximately hundred billion neural connections. The latest trend in unlocking the mysteries of the mind is with the recent advancement in brain-computer interface (BCI) systems. Scientists are emphasizing their research on whether BCI can be augmented with human computer interaction (HCI) to give a new aspiration for restoring independence to neurologically disabled individuals. There are invasive and noninvasive methods for brain signal acquisition such as electroencephalography (EEG), functional MRI (fMRI), electrocorticography (ECoG), calcium imaging, magneto encephalography (MEG), functional near-infrared spectroscopy (fNIRS) and so on. Electroencephalography signals, which are small amounts of electromagnetic waves produced by the neurons in the brain are one of the most popularly used signal acquisition techniques in the existing BCI systems due to their non-invasiveness, easy to use, reasonable temporal resolution and cost effectiveness compared to other brain signal acquisition methods. Electroencephalography is essential for the diagnosis of epilepsy and useful in characterizing various neurological diseases such as Parkinson’s disease, Alzheimer’s disease etc. and also helps in monitoring sleep related disorders. This paper discusses the EEG data processing mechanisms using machine learning techniques and reviews the achievements in this field. Keywords - Brain-Computer-Interface, Brain Signal, Deep Learning, EEG, Human-Computer-Interaction