IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of eeg for brain–computer interface
- 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:[email protected] Independent Component Ensemble of EEG for Brain–Computer Interface
- 2. Abstract—Recently, successful applications of independent component analysis (ICA) to electroencephalographic (EEG) signals have yielded tremendous insights into brain processes that underlie human cognition. Many studies have further established the feasibility of using independent processes to elucidate human cognitive states. However, various technical problems arise in the building of an online brain–computer interface (BCI). These include the lack of an automatic procedure for selecting independent components of interest (ICi) and the potential risk of not obtaining a desired ICi. Therefore, this study proposes an ICi-ensemble method that uses multiple classifiers with ICA processing to improve upon existing algorithms. The mechanisms that are used in this ensemble system include: 1) automatic ICi selection; 2) extraction of features of the resultant ICi; 3) the construction of parallel pipelines for effectively training multiple classifiers; and a 4) simple process that combines the multiple decisions. The proposed ICi-ensemble is demonstrated in a typical BCI application, which is the monitoring of participants’ cognitive states in a realistic sustained-attention driving task. The results reveal that the proposed ICi-ensemble outperformed the previous method using a single ICi with (91.6% versus 84.3%) in the cognitive state classification. Additionally, the proposed ICi-ensemble method that characterizes the EEG dynamics of multiple brain areas favors the application of BCI in natural environments.
- 3. Existing method: First, most existing ICA-based models use a predefined IC. Combining task-related brain processes to characterize complex human behaviors intuitively appears to be a more reasonable method than using only a few specific signals. Second, the order of ICs cannot be determined.
- 4. Proposed method A decision fusion method was then applied to obtain a final result. The parallel processing can be conducted in the proposed ensemble to reduce the computing time. Notably, the ensemble size varies with the number of extracted ICi Merits Complexity is high. Demerits Complexity is low.
- 5. Results Driving simulator and experimental paradigm. (A) High-fidelity VR-based driving environment. (B) nighttime driving scenario. (C) 30-channel EEG cap with sintered Ag/AgCl electrode. (D) Event-related lane-departure paradigm. Deviation onset: time when car begins to deviate from center of cruising lane; Response onset: time when subjects first use steering wheel to compensate for driving error; Response offset: time when car returns to center of cruising lane. (E) EEG segments of 1 s and 90 s of ICi data were used to characterize task performance and vigilance state, respectively, in -trial.