http://iet.metastore.ingenta.com
1887

Robust EEG signal processing with signal structures

Robust EEG signal processing with signal structures

For access to this article, please select a purchase option:

Buy chapter PDF
£10.00
(plus tax if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
Signal Processing and Machine Learning for Brain-Machine Interfaces — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Brain decoding has contributed to the development of cognitive neuroscience and the production of brain-machine interfaces/brain-computer interfaces (BCI/BMI). For brain decoding, electroencephalography (EEG), which allows the observation of the electrophysiological activities of neurons, is widely used to observe brain activity. In particular, an EEG read from electrodes installed on a scalp, which has some advantages when it comes to cost, size, and ease of measurement, is a promising recording method for producing noninvasive BMIs against magnetoencephalograms, functional magnetic resonance imaging, and so on. However, an EEG signal has low spatial resolution and is highly affected by noise. Moreover, the recoding of an EEG is time-consuming and tires BMI users. Therefore, signal processing techniques that can robustly extract brain activity patterns from EEG signals with a low signal-to-noise ratio and a small sample size are necessary. One approach to achieve such techniques is to incorporate additional information retrieved separately from an EEG in signal processing. This chapter describes some techniques that can accomplish this. The signal structures include physical structures, such as the location of the electrodes, and functional structures, such as synchronizing brain regions. The discussion on the importance of the signal structures in a source analysis of EEG signals that can improve the BMI performance will be discussed in this chapter. In contrast to a source analysis, which finds the brain patterns in a source domain, regularization incorporating the signal structures in a sensor domain will be discussed. Moreover, we will show the signal processing in a graph spectral domain that is a vector space derived from the signal structure.

Chapter Contents:

  • Abstract
  • 4.1 Introduction
  • 4.2 Source analysis
  • 4.3 Regularization
  • 4.4 Filtering in graph spectral domain
  • 4.4.1 Graph Fourier transform
  • 4.4.2 Smoothing and dimensionality reduction by GFT
  • 4.4.3 Tangent space mapping from Riemannian manifold
  • 4.4.4 Smoothing on functional brain structures
  • 4.5 Conclusion
  • References

Inspec keywords: electroencephalography; biomedical electrodes; medical signal processing; neurophysiology; brain-computer interfaces

Other keywords: electrodes; electroencephalography; BCI-BMI; EEG signal; brain activity patterns; signal processing; brain regions; brain patterns; signal structure; neuron electrophysiological activities; brain-machine interfaces-brain-computer interfaces; brain decoding; functional magnetic resonance imaging; magnetoencephalograms

Subjects: Electrical activity in neurophysiological processes; Biology and medical computing; User interfaces; Electrodiagnostics and other electrical measurement techniques; Signal processing and detection; Digital signal processing; Bioelectric signals

Preview this chapter:
Zoom in
Zoomout

Robust EEG signal processing with signal structures, Page 1 of 2

| /docserver/preview/fulltext/books/ce/pbce114e/PBCE114E_ch4-1.gif /docserver/preview/fulltext/books/ce/pbce114e/PBCE114E_ch4-2.gif

Related content

content/books/10.1049/pbce114e_ch4
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address