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/

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)

14. Objectives

15. Problem Statement

16. EEG Signal Overview

17. System Architecture

18. Methodology

19. Past, Present, Future Work

20. Time Plan

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

33. 4 – 7

34. Move of Hand

35. Idling

36. These occur during childhood

37. ALPHA

38. 8-12

39. relaxed/reflecting

44. Preprocessing

45. Feature Extraction and Features Selection ( Dimensionality Reduction)

46. Classification

50. Temporal information from a window of data is extracted and then processed using a static classifier.

51. Spatial information alone is indeed powerful enough to produce state-of-the-art performance

54. ICA Block Diagram (2 Signals)Signal #1 Signal #2 a11 a22 a12 a21 Multiplexed Signal #2 Multiplexed Signal #1

55. Independents Components Analysis ICA Sensor #1 Feature #1 Sensor #2 Feature #2 Sensor #3 Feature #3 Sensor #4 Feature #4

57. An information processing paradigm that is inspired by the way biological nervous systems, such as the brain.

59. The neuron sends out spikes of electrical activity through a long axon.

64. For calculating optimal or near optimal weights

66. The number of trainable parameters becomes extremely large.

67. It offers little or no invariance to shifting , scaling and other forms of distortion.

68. The topology of the input data is completely ignored, yielding similar training results for all permutations of the input data.These problems are solved by convolution neural networks concepts.

71. This lead to an implicit reduction of the gap.

72. The sub sampling layers have one trainable weight so number of free parameters in it is lower than in conventional layers.

74. Research about EEG Signal

75. Present

76. Study feature extraction and classification algorithms .

77. Future

79. References David Bouchain “Character Recognition Using Convolutional Neural Networks “. Seminar Statistical Learning Theory University of Ulm, Germany Institute for Neural Information Processing ,2006/2007. Christos Stergiou and DimitriosSiganos” Neural Networks ”. TianLan, DenizErdogmus, Andre Adami, and Michael Pavel, “Feature Selection by Independent Component Analysis and Mutual Information Maximization in EEG Signal Classification”, Department of Biomedical Engineering OGI School of Science and Engineering Oregon Health & Science University Beaverton, Oregon 97006, USA. 4. Rave Harpaz ,” Independent Component Analysis: An Introduction” Pattern Recognition Laboratory The Graduate Center, City University of New York 365 Fifth Avenue New York, NY 10016, USA, Nov/15/ 2005.

80. Thanks