The targets of EEG analysis are to help researchers gain a better understanding of the brain; assist physicians in diagnosis and treatment choices; and to boost brain-computer interface (BCI) technology.
[7] Besides, time domain methods offer a way to on-line measurement of basic signal properties by means of a time-based calculation, which requires less complex equipment compared to conventional frequency analysis.
Specifically, through wavelet decomposition of the EEG records, transient features can be accurately captured and localized in both time and frequency context.
[22] Moreover, the big EEG data, as the input of ANN, calls for the need for safe storage and high computational resources for real-time processing.
[23] EEG, a non-invasive procedure, is used to record brain activity in cognitive studies, different clinical applications and brain-computer interfaces (BCI).
EEG recording is both an easily portable method for different clinical uses and open to applications in various fields as it directly measures collective neural activity.
Careful analyses of the EEG records can provide valuable insight and improved understanding of the mechanisms causing epileptic disorders.
[28] Based on real-time EEG analysis with subject-specific spatial patterns, a brain–computer interface (BCI) can be used to develop a simple binary response for the control of a device.
EEG-based BCI approaches, together with advances in machine learning and other technologies such as wireless recording, aim to contribute to the daily lives of people with disabilities and significantly improve their quality of life.
Brainstorm is a collaborative, open-source application dedicated to the analysis of brain recordings including MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology.