منابع مشابه
Classification of EEG-based motor imagery BCI by using ECOC
AbstractAccuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as variou...
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The neurons in the brain show different firing characteristics during different motor actions and also during motor imagery. The present study tries to observe and classify the variation in neural activity during a motor action and its imagination. Electroencephalogram (EEG) signals from 90 different subjects were used for the same. The data included 3 trials – a base case with eyes opened, a m...
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Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were ...
متن کاملImage Based Approach for Cognitive Classification Using Eeg Signals
The EEG state classifier distinguishes different states and these information are used to understand the normal and abnormal states of users and to adapt their interfaces and add new functionalities. EEG classification is performed conventionally by extracting statistical parameters. But, this classification is affected more by artifacts and hence a better approach using image based is proposed...
متن کاملA latent discriminative model-based approach for classification of imaginary motor tasks from EEG data.
We consider the problem of classification of imaginary motor tasks from electroencephalography (EEG) data for brain-computer interfaces (BCIs) and propose a new approach based on hidden conditional random fields (HCRFs). HCRFs are discriminative graphical models that are attractive for this problem because they (1) exploit the temporal structure of EEG; (2) include latent variables that can be ...
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ژورنال
عنوان ژورنال: Science and Education of the Bauman MSTU
سال: 2014
ISSN: 1994-0408
DOI: 10.7463/0414.0705745