This document discusses developing a generic EEG classification system using brain signals for brain-computer interface applications. The system architecture includes EEG signal acquisition, preprocessing to remove noise and artifacts, feature extraction using independent component analysis and power spectral density, dimensionality reduction, classification using convolutional neural networks, and postprocessing. The goals are to extract spatial and temporal information from EEG signals to classify different brain states and movements like hand movement, tongue movement, walking, eye blinks, and more. This will help build a robust EEG classification system to be used in various BCI applications.

1. EEG Brain Signal Classification for BCI Applications http://eegclassifyandrecognize.blogspot.com/

2. Project MembersSupervisors

3. Pro.DrMostafa Gad-Haqq

4. Pro.DrTareqGharib

5. Dr.HowidaShded

6. Assistants

7. T.A ManalTantawy

8. Team Members

9. Ahmed KhaledAbd El-glil (Information Systems)

10. Ahmed Mohamed Ahmed Mahany (Computer Systems)

11. Islam Ahmed HamedElgarhy (Computer Systems)

12. KamalAshrafKamal El-deen (Information Systems)

13. Mohammed Saeed Ibrahim (Scientific Computing)AgendaProject Overview

14. Objectives

15. Problem Statement

16. EEG Signal Overview

17. System Architecture

18. Methodology

19. Past, Present, Future Work

20. Time Plan

21. ReferencesProject Overview(Objectives)Develop a generic EEG Classification that can be used in different brain computer interface applications. Project Overview(Problem Statement)EEG Signal is a composite signal.EEG Signal OverviewAn electroencephalogram is a measure of the brain's voltage changes as detected from scalp electrodes.

22. Measured in microvolt (µV) .EEG Signal Overview(Cont.)BETA

23. 12-30

24. Thinking

25. Alert & working

26. GAMMA

27. 30-100

28. Short term memory of (sound , tactile,…..)

29. DELTA

30. Up to 4

31. These occur in deep sleep

32. Childhood and in serious organic brain diseasesTHETA