**1. Introduction**

682 Amyotrophic Lateral Sclerosis

Yunusova, Y., Weismer, G., Westbury, J.R. & Lindstrom, M.J. (2008). Articulatory

Yunusova, Y., Green, J.R., Lindstrom, M.J., Ball, L.J., Pattee, G.L. & Zinman, L. (2010).

*of Speech, Language and Hearing Research*, Vol. 51, pp. 596-611

*Disorders*, Vol. 43, pp. 6–20.

movements during vowels in speakers with dysarthria and normal controls. *Journal* 

Kinematics of disease progression in bulbar ALS. *Journal of Communication* 

In this chapter, alternative communication and device control channels, which are helpful for Amyotrophic lateral sclerosis (ALS) patients, are introduced. In this context, human computer interactions (HCIs) will be discussed in three respects; electrical brain activities, eye movements and hemoglobin level in the blood.

With technological advances, fighting or minimization side effects of the diseases is the main purpose of biomedical research. Under this motto, this chapter focuses on HCIs for individuals suffering from motor neuron diseases. ALS is a progressive neurodegenerative disease caused by the degeneration of motor neurons. ALS or other tetraplegic clinical conditions, otherwise known as the locked-in syndrome, have severe disabilities in controlling muscles and consequently have problems in moving the entire body. Some of these patients can only move their eyes. In severe conditions of the progressive motor neuron diseases, patients cannot move their eyes nor can they speak. Establishing an efficient communication channel without overt speaking and hand motions makes the patient's life a bit easier and increases their quality of life.

ALS occurs in between 4 and 8 out of every 100,000 individuals and only a small percentage of cases arise from a known genetic cause (Parker & Parker, 2007). Concerning other motor neuron diseases or speaking and muscular disabilities, there are more than 100 million potential users in need of alternative channels such as brain computer interface (BCI) for communicating with their environment or for controlling devices (Guger, 2008). Considering life span extension and increasing causes of injuries including traffic accidents and explosions, which may result in spinal cord injuries in serious cases, the need for an efficient communication or control channel has been drastically increasing.

HCIs are a research field which includes interactions such as communication and device/machine control between a user and a computer. The aim of the HCI is to improve performance of the interaction, meaning a minimization of the barrier between the human and the computer. Accurate and fast interpretation of what the user wants to do as well as a correct understanding by the computer of the user's intentions or demand is the aim of this research field.

Man-machine interface (MMI), brain-machine interface (BMI) and BCI can be thought of as applications of HCIs. If communication or control is established directly from the brain, it is called BCI and it is the only method of interaction for the individuals with complete

Human Computer Interactions for Amyotrophic Lateral Sclerosis Patients 685

In short, HCI should reflect user demands and expectations accurately and quickly. The next sections of this chapter will introduce the EEG, EOG, NIRS based systems, as they are technologies that show much promise. In addition to these technologies, electrocorticography (ECoG), functional magnetic resonance imaging (fMRI), galvanic skin response (GSR) and heart rate (HR) based systems; and multi-modal integrated design

From a broad perspective, BMI refers to the interface between a brain and a machine (for review Lebedev & Nicolelis, 2006). In this section, the common term - brain computer interface (BCI) will be presented. BCI can be described as a translation of human intentions into a control signal without using the muscles. The aim of BCI is to provide communication

The BCI system translates the signals that are encoded by the user's intentions into messages and control commands. Research in this field has been rapidly growing in neuroscience and bioengineering. Specifically, this technology is promising for users with motor neuron diseases. Table 1 shows the estimated potential users of BCI. According to this table, there

Electrical brain activities (electroencephalography, EEG) related to human intentions can be monitored using electrodes attached to the scalp surface, non-invasively. EEG signals are gross potential of the thousands of neurons, roughly reflecting bodily functions. Because the skull and scalp play the role of a barrier for the electrical signals, EEG signals have low amplitudes (in micro-volts scale) and exist in the 0.5–30 Hz frequency band. Figure 1 shows the ongoing time series EEG signal and its power spectral density. As it is shown from this

In order to increase efficiency, brain electrical signals can be recorded by subdural electrodes (electrocorticogram, ECoG), invasively. ECoGs are neuronal activity that is acquired from smaller cortical areas when compared to EEGs. Epidural or subdural recording is less invasive than intra-cortical recording. While their applications are difficult, the resolution of these recordings can be significantly higher than conventional EEG. The

**Type of the Disease Number of Patients**  Amyotrophic Lateral Sclerosis 400,000/3,000,000

Multiple Sclerosis 2,000,000 Muscular Dystrophy 1,000,000 Brainstem Stroke 10,000,000 Cerebral Palsy 16,000,000 Spinal Cord Injury 5,000,000 Postpolio Syndrome 7,000,000 Guillain-Barre Syndrome 70,000 Other types of Stroke 60,000,000

rationale are introduced briefly.

**2.1 Brain computer interfaces** 

and control for people with severe motor disabilities.

are more than 100 million potential users in the world.

Table 1. Potential users of BCI in the world (Guger, 2008).

figure, 8-12 Hz ( band) and 26 Hz ( band) components are dominant.

BCI usefulness of intra-cortical signals is promising (Wolpaw, 2003).

paralysis. Because these research fields are new, there is a need for development in terms of efficiency; meaning accuracy, reliability and quick responses are necessary. Many research groups from all over the world are focusing on HCI applications in order to improve alternative communication channels for the disabled. An efficient alternative channel for communication and control device without overt speaking and muscular movements is important to make life easier for individuals who are suffering from ALS or other illnesses that prevent proper limb and muscular responses. Because of this, the area of study related with HCIs has high expectations and are important for improving quality of life.

In this chapter, ALS related HCI in particular, is discussed. The very common field electroencephalogram (EEG) based BCI and other approaches in this field are presented. With interdisciplinary studies, developing new interfaces and interaction techniques are opening new research fields for investigation. Especially for the paralysis patients, the classical communication or control ways, such as overt speaking or hand motions cannot be used. Using bio-signals such as EEG and its various methodologies (i.e. P300, slow cortical potentials, etc), electrooculogram (EOG), hearth rate (HR) or galvanic skin response (GSR) as well as the hemoglobin level which is related to oxygenation, are the only ways to send messages or control signals to devices regarding user's demands, intentions or expectations. This does not only give the patients the potential ability to give messages on computer screens and control a powered wheelchair or robot arm without muscular movements, but also can be potentially useful for the elderly as well.

The aim of this chapter is to present the state of the art of the technology on HCIs. This chapter also addresses the use of different bio-signals individually or the integrated hybrid/integrated multi-modal system approach for communication and control with high performance. In order to increase performance, processing combined bio-signals and multi-modal integrated systems will be discussed. For this purpose, several bio-signals such as EEG, EOG, and functional near infrared (fNIR) spectroscopy based system research are introduced.
