Brain-Machine Interface: Human-Computer Interaction

Brain-Machine Interface: Human-Computer Interaction

Manoj Kumar Mukul (BIT Mesra, India) and Sumanta Bhattaharyya (BIT Mesra, India)
DOI: 10.4018/978-1-5225-2515-8.ch018
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Abstract

The brain-machine interface (BMI) is a very recent development in the area of the human machine interaction (HCI) and emerged as the sister technology of BCI. A physiological signal related to these electrical potentials in response of the mental thoughts is known as Electroencephalogram (EEG) signals. The BMI is most commonly known as the BCI because there is a direct communication between the brain and the external machine via a computer, which analyses and interprets the incoming physiological signals, which contain the shadow of the mental activity and the different types of artefacts. A multi-channel recording of the electromagnetic waves emerging from the neural currents in the brain generate a large amounts of the EEG data. The neural activity of the human brain recorded non-invasively is sufficient to control the external machine, if advanced methods of signal analysis and feature extraction are used in combination with the machine learning techniques either supervised or unsupervised.
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Introduction

Many patients become afflicted with neurological conditions or neurodegenerative diseases (Wolpaw et. al., 2000) hat disrupt the normal information flow from the brain to the spinal cord, and eventually, to the targets of that information,(i.e., the muscles) which affect the person’s intent. Amyotrophic lateral sclerosis (ALS also called Lou Gehrig’s disease),spinal cord injury, strokes, and many other conditions can impair either the neural pathways controlling muscle’s, or impair the muscles themselves. Individuals that are most affected may lose all their abilities to control their muscles. Thus, they lose all options to communicate and become completely locked inside their bodies. In absence of ways to reverse the effects of these disorders, there are three principal options for restoring function. The first option is to substitute the damaged neural pathways or muscles with pathways or muscles that are still functional. While this substitution is often limited, it can still be useful. For example, patients can use eye movements to communicate or hand movements to produce synthetic speech. The second option is to restore function by detecting nerve or muscle activity above the level of the injury. For example, freehand prosthesis is a method of restoring hand function to patients with spinal cord injuries. The third option for restoring function is to provide the brain with a new and non-muscular output channel, a brain–computer interface (BCI) (Wolpaw et.al., 2000), for conveying the user’s intent to the external world.

The brain-machine interface (BMI) is a very recent development in the area of the human machine interaction (HCI) and emerged as the sister technology of BCI. However, there is not a clear cut difference between the BCI and the BMI. In fact, a computer is always between the brain and the machine for the interface. Different mental thoughts generate the electrical potentials over the surface of the scalp. These are recorded with sensors placed on the surface of the head. A form of physiological signals related to these electrical potentials of thoughts are known as Electro-encephalogram (EEG) (Carlson, 2007) signals. The BMI based on the EEG signals is called the EEG-based BMI (Carlson, 2007) system. The BMI pertains to the manipulation or operation of an external machine as per thoughts of a user, and such machine is called a though controlled machine. Thus, there is a direct communication between human and machine via a computer. The BMI is most commonly known as the BCI because there is a direct communication between the brain and the external machine via a computer, which analyses and interprets the incoming physiological signals (electroencephalograms), which contain the shadow of the mental activity and the different types of artifacts. A multi-channel recording of the electromagnetic waves emerging from the neural currents in the brain shows large amounts of EEG data. The neural activity of the human brain recorded non-invasively is sufficient to control the external machine, if advanced methods of signal analysis and feature extraction are used in combination with the machine learning techniques either supervised or unsupervised. A suitable feature extraction and classification methods are useful to generate a control command for controlling the external machine.

BCI provides a direct interface between the human brain and a computer. BCI is an emerging application of HCI. The first international BCI workshop was held in June 1999, in Rensselaerville, New York. More than twenty research groups from the different part of the world participated in that workshop. In it, a formal definition of the BCI was proposed (Wolpaw, 2000):

A brain-computer interface is a communication system that does not depend on the brain’s normal output pathways of peripheral nerves and muscles.

Key Terms in this Chapter

Cohen’s Kappa Coefficient: Cohen's kappa coefficient is a statistic which measures inter-rater agreement for qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculation, since ? takes into account the possibility of the agreement occurring by chance.

Blind Source Separation: Blind signal separation (BSS), also known as blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information (or with very little information) about the source signals or the mixing process.

Brain-Computer Interface: A Brain Computer Interface is a communication system that does not depend on the Brain’s normal output pathways of peripheral nerves and muscles. Brain computer interface (BCI) is a collaboration between a brain and a device that enables signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb. The interface enables a direct communications pathway between the brain and the object to be controlled.

Electroencephalogram: An electroencephalogram (EEG) is a test that detects electrical activity in your brain using small, flat metal discs (electrodes) attached to your scalp.

Mutual Information: In probability theory and information theory, the mutual information (MI) of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the “amount of information” (in units such as bits) obtained about one random variable, through the other random variable.

Classification Accuracy: It is the number of correct predictions made divided by the total number of predictions made, multiplied by 100 to turn it into a percentage.

Independent Component Analysis: Independent component analysis (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals.

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