مشخصات مقاله:
عنوان فارسی مقاله:
یک الگوی فضایی رایج ارتقا یافته در ترکیب با استراتژی انتخاب کانال برای تشخیص احساسات مبنی بر الکتروانسفالوگرافی
عنوان انگلیسی مقاله:
An improved common spatial pattern combined with channel-selection strategy for electroencephalography-based emotion recognition
کلمات کلیدی مقاله:
الگوی فضایی مشترک (CSP)، تشخیص احساسات، مورب سازی تقریب مشترک (JAD)، انتخاب کانال
مناسب برای رشته های دانشگاهی زیر:
مهندسی پزشکی
وضعیت مقاله انگلیسی و ترجمه:
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فهرست مطالب:
Abstract
Keywords
1. Introduction
2. Materials
2.1. EEG Generation
2.2. Emotion expression
2.3. Datasets
2.3.1. Stimuli selection
2.3.2. Experimental paradigm design
3. Methods
3.1. Data preprocessing
3.2. Feature extraction using the improved CSP
3.2.1. Solution for multiclass CSP
3.2.2. Improved multiclass CSP
3.3. Channel selection strategy
4. Analysis of the experiment results
4.1. Determination of the optimum parameters for CSP
4.2. Performance evaluation of emotion recognition
4.2.1. Verification of channel selection strategy
4.2.2. Experiment results on different datasets
4.3. Comparison experiments
5. Conclusions
Conflicts of Interest
Acknowledgements
References
قسمتی از مقاله انگلیسی:
1. Introduction
Human-computer interaction (HCI) refers to the process of information exchange between a user and a computer, with the user using a certain “dialogue” language to interact with the computer to complete certain tasks [1]. Currently, intelligent HCI systems such as intelligent cars, intelligent voice navigation, intelligent medical equipment, and intelligent homes, are rapidly enriching our daily lives [2–4]. Such systems can achieve their corresponding functions according to the user commands well. However, adjusting their interaction mode based on the psychological state of a user is problematic, owing to poor emotion perception. It is difficult to realize a true “intelligent interaction”, which severely restricts the functions and applications of HCI systems. Therefore,the development of an HCI system with emotional intelligence has become an important research area in the fields of artificial intelligence and cognitive science [5]. For the implementation of an emotional HCI system, the acquisition and recognition of human emotion information is a key step. To achieve this objective, researchers have conducted a series of studies in recent years. Based on the signal acquisition technique, emotion recognition methods can be classified into two categories: contact and contact-free. Currently, contact-free methods are mainly implemented based on human facial expressions or speech. Among them, a speech-based method perceives the emotional states of a user by extracting emotion-related features, such as the tone, energy, and spectrum [6,7]. Similarly, a facial expression method is primarily concerned with the emotional information corresponding to the variations in facial features [8,9]. These features generally consist of static information (e.g., skin color), slowly varying information (e.g., permanent wrinkles), or rapidly varying information (e.g., opening of the mouth or raising of the eyebrows) with respect to time. Contact-free methods have the advantages of simple signal acquisition and being easy for the users. However, when the users attempt to mask their emotion, the real emotional state may be inconsistent with the external presentation. In such a case, contact-free methods have difficulty in obtaining a correct recognition. Owing to the non-deceptiveness of bioelectrical signals, contact methods have received increasing attention for identifying the emotional states of human users [10–12]. In general, the peripheral bioelectrical signals collected from the autonomic nervous system refer to those of electrocardiogram, blood pressure, skin conductance, body surface temperature, and respiration, among other parameters.