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Intelligent Machine Learning Based EEG Signal Classification Model

computers, Materials & Continua 2022年第4期71卷 1821-1835页

作者： Mesfer Al Duhayyim Haya Mesfer Alshahrani Fahd N.Al-Wesabi Mohammed Abdullah Al-Hagery Anwer Mustafa Hilal Abu Sarwar Zaman Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of computer Science College of ComputerQassim UniversitySaudi Arabia Department of computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

In recent years,Brain-computer interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,etc.Electroencephalography(EEG)is a technique commonly applied in capturing brain signals.It is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and cost-effective.EEG classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and classification.With this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI system.Independent Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG information.Besides,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG signals.Moreover,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was conducted.The outcomes were examined under distinct aspects.The experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.

关键词： Brain computer interface EEG recognition human computer interface machine learning parameter tuning FSVM

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Rough set based multi-agent system cooperation for industrial supervisory interface system

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Journal of Shanghai University(English Edition) 2006年第6期10卷 526-530页

作者：王滔费敏锐雷电 School of Mechatronics Engineering and Automation Shanghai University

In this paper, rough set theory is introduced into the interface multi-agent system （MAS） for industrial supervisory system. Taking advantages of rough set in data mining, a cooperation model for MAS is built. Rules for avoiding cooperation conflict are deduced. An optimization algorithm is used to enhance security and real time attributes of the system. An application based on the proposed algorithm and rules are given.

关键词： rough set multi-agent system（MAS） cooperation human computer interface industrial control system.

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An acoustic interface for triggering actions in virtual environments

An acoustic interface for triggering actions in virtual envi...

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Fourth International Conference on Virtual Reality and Its Applications in Industry

作者： Christoph Groenegress Jochen Denzinger MARS-Exploratory Media Lab Wolfgang Strauss and Monika Fleischmann Wolfgang Strauss and Monika Fleischmann Fraunhofer Institute for Media Communication Sankt Augustin Germany

＜正＞Currently one of the main research issues in human computer Interaction （HCI） is to develop more intuitive, multimodal and natural interfaces. Among them, the interface for triggering simple actions or selecting objects in virtual environments （VEs） is one of the concerned areas. In this paper we describe an acoustic interface which uses finger snap or hand clap sounds as the input command to initiate events for VE applications. We developed a sophisticated algorithm based on the wavelet transform and neural network techniques, which separate the environment noise from the snap and clap sound. The acoustic interface could be integrated with other interfaces like optical tracking systems to provide a more natural, easy-to-use, efficient and body-centered multimodal interaction for virtual reality applications.

关键词： human computer interface Acoustic interface Wavelet Algorithm Neural Network Virtual Environment Natural and Multimodal interfaces.

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