AIMC Topic: Electroencephalography

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Optimized echo state networks with leaky integrator neurons for EEG-based microsleep detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The performance of a microsleep detection system was calculated in terms of its ability to detect the behavioural microsleep state (1-s epochs) from spectral features derived from 16-channel EEG sampled at 256 Hz. Best performance from a single class...

Online control of a humanoid robot through hand movement imagination using CSP and ECoG based features.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Intention recognition through decoding brain activity could lead to a powerful and independent Brain-Computer-Interface (BCI) allowing for intuitive control of devices like robots. A common strategy for realizing such a system is the motor imagery (M...

EEG error potentials detection and classification using time-frequency features for robot reinforcement learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In thought-based steering of robots, error potentials (ErrP) can appear when the action resulting from the brain-machine interface (BMI) classifier/controller does not correspond to the user's thought. Using the Steady State Visual Evoked Potentials ...

Emotion recognition from sound stimuli based on back-propagation neural networks and electroencephalograms.

The Journal of the Acoustical Society of America
This research aims to explore the feasibility of using back-propagation (BP) neural networks and electroencephalograms (EEGs) to recognize the emotional reactions induced by sound stimuli in the dimensions of pleasure and arousal, as well as compare ...

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy.

Chaos (Woodbury, N.Y.)
In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to charac...

[Automatic Sleep Staging Method Based on Energy Features and Least Squares Support Vector Machine Classifier].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
The research of sleep staging is not only the basis of diagnosing sleep related diseases, but also the precondition of evaluating sleep quality, and has important clinical significance. In recent years, the research of automatic sleep staging based o...

[Study on Sleep Staging Based on Support Vector Machines and Feature Selection in Single Channel Electroencephalogram].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Sleep electroencephalogram (EEG) is an important index in diagnosing sleep disorders and related diseases. Manual sleep staging is time-consuming and often influenced by subjective factors. Existing automatic sleep staging methods have high complexit...

An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand.

Biomedizinische Technik. Biomedical engineering
The loss of hand function can result in severe physical and psychosocial impairment. Thus, compensation of a lost hand function using assistive robotics that can be operated in daily life is very desirable. However, versatile, intuitive, and reliable...

Convergent Cross Mapping: Basic concept, influence of estimation parameters and practical application.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In neuroscience, data are typically generated from neural network activity. Complex interactions between measured time series are involved, and nothing or only little is known about the underlying dynamic system. Convergent Cross Mapping (CCM) provid...

Seizure detection using regression tree based feature selection and polynomial SVM classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients with low hardware complexity and low power consumption. In the proposed approach, we first compute the spectrogram of the input fragmented EEG sign...