- 5.1 Introduction 75
- 5.2 Importance of Studying Drug Addiction 76
- 5.3 Problem Formulation 77
- 5.4 Research Design 78
- 5.4.1 Hypothesis 78
- 5.4.2 Sample Size Computation 78
- 5.4.3 Subjects Details 80
- 5.5 Experiment Procedure 81
- 5.6 Software/Hardware Details 82
- 5.7 Data Description 83
- 5.7.1 Experiment Data Accompanying This Chapter 83
- 5.8 Relevant Papers 83
According to the World Health Organization (WHO),1 alcohol misuse is common among primary care patients, and results in considerable suffering, mortality, and economic costs (World Health Organization, 2011). Alcohol use is categorized as unsafe drinking if alcohol consumption exceeds 48 g per day or 144 g per week (Parsons and Nixon2). Drinking severity can be classified into heavy drinking, alcohol abuse (AA), and alcohol dependence (AD). Both AA and AD are described distinctly, according to the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV),3 as a severe form of alcohol drinking that causes distress or harm to drinker. As defined in the DSM-IV, AA is indicated as the recurring use of alcohol despite its negative consequences such as social, interpersonal, and legal problems. AD or alcoholism is the most severe form of alcohol use and is characterized by an increased tolerance and physical dependence on alcohol. People with AA and AD are referred to as alcohol abusers and alcoholics, respectively. In this study, the two DSM-IV disorders, AA and AD, will be referred to as alcohol use disorder (AUD).
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Designing EEG Experiments for Studying the Brain.
Screening and assessment of alcohol-related problems are mainly based on self-reports. For screening purposes, it has been concluded that only using self-reports is insufficient and there is a need to incorporate additional methods. In addition, accuracy of self-reporting has been questioned, especially for heavy drinkers, and people tended to underreport alcohol consumption quantity. Electroencephalography (EEG) is a brain imaging technique that can directly capture brain electrical voltage potentials over the scalp.4 The variations in the brain electrical potentials reflecting the changes inside the brain neuronal networks occur due to any stimulation. The EEG can be analyzed using computational techniques to extract useful information for assessment of changes in neuronal net- works.4,5 It is reported that EEG has the potential to discriminate between alcoholics and control subjects.6 Therefore, in this chapter we focus on the use of EEG to discriminate among AA, AD, and normal controls.