seminar

1. In The Name Of God
Student: Monireh Tavakoli
Supervisor: Dr. Mohammad Zadeh

2. Spatial Filtering In EEG Signal Processing
2

3.  Contents
Introduction
 History
 Mathematical Model
category
Results
Conclusion
Future Work 3

4.  Introduction
Table1 . Data acquisition techniques
EEG
 Simplicity
 usability
 provides high temporal Resolution
 low cost
 noninvasive sensors
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Figure1. Data acquisition techniques

5.  Introduction
Spatial Filtering algorithms: These are methods that combine
several channels into a single one, generally using weighted
linear combinations, from which features will be extracted
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Figure2- a)EEG signal are very noisy. b) Combination EEG of Multichannel
a)
b)

6.  History
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The detection of epileptiform discharges
(ED’s) in the EEG is an important
component in the diagnosis of epilepsy
Figure 3- EEG is Mixed signals from different sources

7.  History
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8.  History
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Figure 4- block Diagram of BCI

9.  Mathematical Model
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𝑥 = 𝑖 𝑤𝑖𝑥𝑖 = 𝑤X (1)
𝑥 : Filtered Signal
𝑥𝑖∶ i th channel signal
𝑤𝑖: wheight of i th channel
X: Matrix of all Data

10.  category
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Spatial
Filter
Data
Driven
Weight
Supervised Unsupervised
Fixed
Weight

11.  category
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• Bipolar
• Small & Large
Laplacian
• Inverse Solution
Fixed
Weights
• ICA
• PCA
• CSP (Supervised)
Data
Driven

12. Figure 5- Comparisons of different classifiers across selected
spatial filters using 3 channels [2]
 Results
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13. Figure 6-Classification performance (rate of accurate classification)
obtained on data set IIa for “BCI competition IV”in classifying imagined
movement of the right hand using different spatial filters
 Results
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14.  Conclusion
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 Electrodes are separate but they are not independent
 Single electrode is insufficient to isolate different neural generators.
 different sources have different spatial-spectral-temporal characteristics
 The idea of a spatial filter is to use weighted combinations of electrode
activity to identify patterns in the data
 The weights are defined by statistical and/or anatomical criteria
 goal is isolating sources of variance in multichannel data
 Commonly used classes of spatial filtering include independent
components analysis (ICA), the surface Laplacian, and source
localization methods such as dipole-fitting, LORETA, and beamforming

15.  Future Work
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Combination with other Method
Work on Animal specimens
 Define some New Weights for fixed Weighted filters
 Use Spatial Filters in Cognitive Science

16.  Refrences
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[1] Cohen, Michael X. "Comparison of linear spatial filters for identifying oscillatory activity in
multichannel data." Journal of neuroscience methods 278 (2017).
[2] Lotte, Fabien. "A tutorial on EEG signal-processing techniques for mental-state recognition in brain–
computer interfaces." Guide to Brain-Computer Music Interfacing. Springer, London, 2014.
[3] Rahman, M. K. M., and Md A. Mannan Joadder. "A review on the components of EEG-based motor
imagery classification with quantitative comparison." Appl Theory Comput Technol 2.2 (2017).
[4] Aggarwal, Swati, and Nupur Chugh. "Signal processing techniques for motor imagery brain computer
interface: A review." Array 1 (2019).
[5] Wang, Zhengwei, et al. "A review of feature extraction and classification algorithms for image RSVP
based BCI." Signal processing and machine learning for brain-machine interfaces (2018).

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