scholarly journals EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform

2021 ◽  
pp. 144-149
Author(s):  
Gokhan Altan ◽  
Gulcin Inat
Keyword(s):  
Wavelet Transform ◽  
Spatial Attention ◽  
Event Related Potentials ◽  
Frequency Domain Analysis ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Time Frequency ◽  
Empirical Wavelet Transform ◽  
Related Potentials ◽  
Attention Shifts

The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.

scholarly journals EEG based Spatial Attention Shifts Detection using Time-Frequency features on Empirical Wavelet Transform

2021 ◽  
pp. 144-149
Author(s):  
Gokhan Altan ◽  
Gulcin Inat
Keyword(s):  
Wavelet Transform ◽  
Spatial Attention ◽  
Event Related Potentials ◽  
Frequency Domain Analysis ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Time Frequency ◽  
Empirical Wavelet Transform ◽  
Related Potentials ◽  
Attention Shifts

The human nervous system has over 100b nerve cells, of which the majority are located in the brain. Electrical alterations, Electroencephalogram (EEG), occur through the interaction of the nerves. EEG is utilized to evaluate event-related potentials, imaginary motor tasks, neurological disorders, spatial attention shifts, and more. In this study, We experimented with 29-channel EEG recordings from 18 healthy individuals. Each recording was decomposed using Empirical Wavelet Transform, a time-frequency domain analysis technique at the feature extraction stage. The statistical features of the modulations were calculated to feed the conventional machine learning algorithms. The proposal model achieved the best spatial attention shifts detection accuracy using the Decision Tree algorithm with a rate of 89.24%.

Adapted wavelet transform improves time-frequency representations: a study of auditory elicited P300-like event-related potentials in rats

2017 ◽  
Vol 14 (2) ◽  
pp. 026012 ◽  
Author(s):  
Nelly Richard ◽  
Bettina Laursen ◽  
Morten Grupe ◽  
Asbjørn M Drewes ◽  
Carina Graversen ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Event Related Potentials ◽  
Time Frequency ◽  
Related Potentials

Time–Frequency Analysis of Single-Sweep Event-Related Potentials by Means of Fast Wavelet Transform

1999 ◽  
Vol 66 (1) ◽  
pp. 129-145 ◽  
Author(s):  
Tamer Demiralp ◽  
Juliana Yordanova ◽  
Vasil Kolev ◽  
Ahmet Ademoglu ◽  
Müge Devrim ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Frequency Analysis ◽  
Event Related Potentials ◽  
Time Frequency Analysis ◽  
Fast Wavelet Transform ◽  
Time Frequency ◽  
Fast Wavelet ◽  
Related Potentials

Brain Responses to World Knowledge Violations: A Comparison of Stimulus- and Fixation-triggered Event-related Potentials and Neural Oscillations

2015 ◽  
Vol 27 (5) ◽  
pp. 1017-1028 ◽  
Author(s):  
Paul Metzner ◽  
Titus von der Malsburg ◽  
Shravan Vasishth ◽  
Frank Rösler
Keyword(s):  
Word Meaning ◽  
Event Related Potentials ◽  
Visual Presentation ◽  
World Knowledge ◽  
N400 Effect ◽  
Brain Potentials ◽  
Time Frequency ◽  
Gamma Range ◽  
Brain Responses ◽  
Related Potentials

Recent research has shown that brain potentials time-locked to fixations in natural reading can be similar to brain potentials recorded during rapid serial visual presentation (RSVP). We attempted two replications of Hagoort, Hald, Bastiaansen, and Petersson [Hagoort, P., Hald, L., Bastiaansen, M., & Petersson, K. M. Integration of word meaning and world knowledge in language comprehension. Science, 304, 438–441, 2004] to determine whether this correspondence also holds for oscillatory brain responses. Hagoort et al. reported an N400 effect and synchronization in the theta and gamma range following world knowledge violations. Our first experiment (n = 32) used RSVP and replicated both the N400 effect in the ERPs and the power increase in the theta range in the time–frequency domain. In the second experiment (n = 49), participants read the same materials freely while their eye movements and their EEG were monitored. First fixation durations, gaze durations, and regression rates were increased, and the ERP showed an N400 effect. An analysis of time–frequency representations showed synchronization in the delta range (1–3 Hz) and desynchronization in the upper alpha range (11–13 Hz) but no theta or gamma effects. The results suggest that oscillatory EEG changes elicited by world knowledge violations are different in natural reading and RSVP. This may reflect differences in how representations are constructed and retrieved from memory in the two presentation modes.

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