Current Practices in Electroencephalogram- Based Brain-Computer Interfaces

Current Practices in Electroencephalogram- Based Brain-Computer Interfaces

Ramaswamy Palaniappan (University of Essex, UK), Chanan S. Syan (University of the West Indies, West Indies, Trinidad and Tobago) and Raveendran Paramesran (University of Malaya, Malaysia)
Copyright: © 2009 |Pages: 14
DOI: 10.4018/978-1-60566-026-4.ch143
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Abstract

Electroencephalogram (EEG) is the electrical activity of the brain recorded by electrodes placed on the scalp. EEG signals are generally investigated for the diagnosis of mental conditions such as epilepsy, memory impairments, and sleep disorders. In recent years there has been another application using EEG: for brain-computer interface (BCI) designs (Vaughan & Wolpaw, 2006). EEG-based BCI designs are very useful for hands-off device control and communication as they use the electrical activity of the brain to interface with the external environment, therefore circumventing the use of peripheral muscles and limbs. Some current applications of BCIs in communication systems are for paralyzed individuals to communicate with their surroundings through character/menu selection and in device control such as wheelchair movement, prosthetics control, and flight and rehabilitative (assistive) technologies. For the general public, some of the possible applications are hands-off menu selection, flight/space control, and virtual reality (entertainment). BCI has also been applied in biometrics (Palaniappan & Mandic, 2007).
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Introduction

Electroencephalogram (EEG) is the electrical activity of the brain recorded by electrodes placed on the scalp. EEG signals are generally investigated for the diagnosis of mental conditions such as epilepsy, memory impairments, and sleep disorders. In recent years there has been another application using EEG: for brain-computer interface (BCI) designs (Vaughan & Wolpaw, 2006).

EEG-based BCI designs are very useful for hands-off device control and communication as they use the electrical activity of the brain to interface with the external environment, therefore circumventing the use of peripheral muscles and limbs. Some current applications of BCIs in communication systems are for paralyzed individuals to communicate with their surroundings through character/menu selection and in device control such as wheelchair movement, prosthetics control, and flight and rehabilitative (assistive) technologies. For the general public, some of the possible applications are hands-off menu selection, flight/space control, and virtual reality (entertainment). BCI has also been applied in biometrics (Palaniappan & Mandic, 2007).

This research area is extremely exciting, and in recent times, there has been an explosive growth of interest in this revolutionary new area of science which would enable computers (and therefore any other reactive device) to be controlled by thought alone—the benefits for the severely disabled would be truly astonishing. For example, in 1990, there were less than 10 groups (mostly in the U.S.) with research interests in BCI; but this has grew to more than 130 groups worldwide in 2004 (Vaughan & Wolpaw, 2006). It is a multidisciplinary field comprising areas such as computer and information sciences, engineering (electrical, mechanical, and biomedical), neuroscience, and psychology. State-of-the-art BCI designs are still very primitive, but because of their potential to assist the disabled, there is an increasing amount of investment in their development.

This article will give an overview of the general elements in a BCI system and existing BCI methodologies, state the current applications of BCI devices in communication system and device control, and describe the current challenges and future trends in BCI technology. (Figure 1)

Figure 1.

Main elements of general BCI system

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Background

In general, a BCI system comprises five stages: data collection, pre-processing, feature extraction, decision making (which includes translation algorithm1), and device command. Normally, the pre-processing, feature extraction, and decision-making stages are done using a computer, though a dedicated hardware could be designed for this purpose. Sometimes, these five stages can be simplified to just three: sensor, decoder, and actuator (Hochberg & Donoghue, 2006).

Key Terms in this Chapter

Slow Cortical Potentials: The potential shifts in EEG (around 1-2 Hz), which can last several seconds.

Steady State VEP: A type of VEP caused by photic response (frequency following effect).

P300 Component: The third positive component in visual evoked potential; normally evoked around 300 ms after stimulus onset.

Electroencephalogram (EEG): Brain activity obtained as recorded signals from the scalp using electrodes.

Brain-Computer Interface/Brain-Machine Interface (BCI/BMI): Devices that use electroencephalogram signals to perform a communication or control action.

Electrodes (channels): Sensors normally made of Ag/AgCl that are used to record electroencephalogram.

Visual Evoked Potential (VEP): An EEG component that is in response (i.e., evoked) by a visual stimulus modality.

Biometrics: Identification or authentication of the individuality of a person using the behavioral or physiological characteristics of the person.

Motor Imagery: Used in BCI designs where subjects imagine moving a limb.

Mental Activity/Task: Any task that generates EEG for use in BCI designs.

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