scholarly journals A New Emotional Classification Method Based on the Combined Characteristics of EEG Signals

2018 ◽  
Keyword(s):  
Classification Method ◽  
Eeg Signals

scholarly journals Mental Workload Classification Method Based on EEG Independent Component Features

2020 ◽  
Vol 10 (9) ◽  
pp. 3036 ◽  
Author(s):  
Hongquan Qu ◽  
Yiping Shan ◽  
Yuzhe Liu ◽  
Liping Pang ◽  
Zhanli Fan ◽  
...  
Keyword(s):  
Human Resources ◽  
Classification Accuracy ◽  
Mental Workload ◽  
Independent Component ◽  
Classification Method ◽  
Speech Signals ◽  
Eeg Signals ◽  
Independent Components ◽  
Brain Signals ◽  
Electroencephalogram Eeg

Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

An Automatic Classification Method for Involuntary and Two Types of Voluntary Blinks

2016 ◽  
Vol 136 (9) ◽  
pp. 1350-1358 ◽  
Author(s):  
Hironobu Sato ◽  
Kiyohiko Abe ◽  
Shoichi Ohi ◽  
Minoru Ohyama
Keyword(s):  
Automatic Classification ◽  
Classification Method

Bone Density Classification Method for Robotic Drilling System

2013 ◽  
Vol 133 (3) ◽  
pp. 328-334 ◽  
Author(s):  
Koyo Yu ◽  
Yuki Saito ◽  
Yusuke Kasahara ◽  
Hiromasa Kawana ◽  
Shin Usuda ◽  
...  
Keyword(s):  
Bone Density ◽  
Classification Method ◽  
Robotic Drilling ◽  
Drilling System

NON-INVASIVE BCI METHOD: EEG - ELECTROENCEPHALOGRAPHY

2019 ◽  
pp. 139-145
Author(s):  
Selma Büyükgöze
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Electrode Placement ◽  
Eeg Signals ◽  
Non Invasive ◽  
Brain Signals ◽  
Brain States ◽  
The Brain

Brain Computer Interface consists of hardware and software that convert brain signals into action. It changes the nerves, muscles, and movements they produce with electro-physiological signs. The BCI cannot read the brain and decipher the thought in general. The BCI can only identify and classify specific patterns of activity in ongoing brain signals associated with specific tasks or events. EEG is the most commonly used non-invasive BCI method as it can be obtained easily compared to other methods. In this study; It will be given how EEG signals are obtained from the scalp, with which waves these frequencies are named and in which brain states these waves occur. 10-20 electrode placement plan for EEG to be placed on the scalp will be shown.

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