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The effort to integrate emotions into human-computer interaction (HCI) system has attracted broad attentions. Automatic emotion recognition enables the HCI to become more intelligent and user friendly. Although numerous studies have been performed in this field, emotion recognition is still an extremely challenging task, especially in real-world practice usage. In this work, probabilistic neural network...
In recent years, the detection of drowsiness based on Electroencephalogram (EEG) signal has been paid great attentions. Most of the popular algorithms used for Brain Computer Interface (BCI) applications are, the Support Vector Machine (SVM) and the Artificial Neuronal Network (ANN)). The challenge is to developed a drowsiness detection system that is at once adapt to an embedded implementation and...
The main principle behind EEG-based brain computer interfaces (BCI) is the recording and accurate classification of EEG signals during imagination of different types of motor movements. The changes in the neural activity effected by motor imagery are a lot similar to those induced by actual movement. Common features, e.g., band power values, present in the single EEG trials are extracted by suitable...
This paper reports the investigations and experimental procedures conducted for designing an automatic sleep classification tool basedconly in the features extracted with wavelets from EEG, EMG and EOG (electro encephalo-mio- and oculo-gram) signals, without any visual aid or context-based evaluation. Real data collected from infants was processed and classified by several traditional and bio-inspired...
Brain Computer Interface Systems (BCIs) allow the identification of volitive brain activity patterns. This allows their use as input channels for alternative communication and computer access systems by patients suffering from severe motor disabilities. This paper presents preliminary results obtained after extracting four different features from EEG signals in order to recognize the activity patterns...
This paper improves Biomimetic Pattern Recognition based on Hyper Sausage Neuron and applies it in the study of Motor Imagery EEG recognition. The paper uses the datasets from previous Brain-Computer Interface Competitions to test the accuracy and efficiency of the results, and compares them with those of SVM and BP. The results show that: with sufficient training set, the performance of Biomimetic...
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