Open AccessA literature review of brain decoding research
Author(s) -
Rolly Maulana Awangga,
Tati L. R. Mengko,
Nugraha Priya Utama
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/830/3/032049
Subject(s) - decoding methods , brain activity and meditation , sensory system , visual cortex , computer science , neural decoding , somatosensory system , brain–computer interface , object (grammar) , neuroscience , human brain , psychology , electroencephalography , artificial intelligence , telecommunications
Brain Decoding is a popular topic in neuroscience. The purpose is how to reconstruct an object that came from a sensory system using brain activity data. There is three brain area generally use in brain decoding research. The somatosensory area generally using mice and touch they whisker. Auditory area using different sound frequency as stimuli. The visual area using shape, random image, and video. Take one example in the visual cortex. Using the retinotopic mapping concept, the object possible to reconstruct using visual cortex activity recorded by fMRI. Retinotopic mapping focus is to relate fMRI records into visual objects seen by the subject. This brain possibilities of decoding research come to the next level when combining using deep learning. The image seen by the subject can be reconstructed by using visual cortex activity. Make reconstruction come faster and realistic to predict the stimuli. This opportunity is opening the era of the brain-computer interface. Combine a method to analyze brain functionality related to the human sensory. Bring hope and increased human quality of life. This paper reviews research in the field of brain encoding. Divide into three sections, the first section is brain decoding research in somatosensory. The second section is brain decoding in the auditory cortex. For the last section, explain visual cortex reconstruction. Every section includes equipment devices to record brain activity and the source of datasets and methods to get the brain activity data.