This chapter focuses on brain computer interface (BCI) brain signal classification. BCI classification is a multistep process which includes: brain signal acquisition: This refers to the brain imaging method used to acquire the brain signal, such as electroencephalography (EEG). Preprocessing during the preprocessing step, various signal processing methods such as digital filtering and artefact removal methods are applied in order to improve signal quality. Feature extraction: during this step useful features in the signal associated with the user's cognitive state are extracted. Classification: This involves the extracted features to make predictions about the user's current cognitive state. This can involve machine-learning techniques or other detection algorithms. Device control: this step, commonly known as `translation', involves converting the classifier outputs into a form usable by the external device.

Chapter Contents:

• 9.1 Introduction to brain signal classification
• 9.1.1 The SSVEP response
• 9.1.2 Normalisation
• 9.2 SSVEP detection methods
• 9.2.1 Correlation-based classification
• 9.2.2 Power-based classification
• 9.3 Previous work
• 9.4 Comparison of normalisation methods
• 9.4.1 Comparison: CCA-based normalisation
• 9.4.2 Comparison: PSD-based normalisation
• 9.5 Discussion
• 9.6 Summary
• References

Inspec keywords: electroencephalography; medical signal processing; brain-computer interfaces; brain; feature extraction

Other keywords: brain computer interface; digital filtering; normalisation; artefact removal methods; electroencephalography; brain signal classification; cognitive state; machine-learning techniques; signal processing methods; preprocessing step; BCI classification; signal quality; brain signal acquisition; feature extraction

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