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Effects of Images with Different Levels of Familiarity on EEG
[article]
2017
arXiv
pre-print
Preserved Fulltext
Then, we evaluated the efficiency of the extracted features by using p-value and also an orthogonal feature selection method (combination of Gram-Schmitt method and Fisher discriminant ratio) for feature ...
The best results of classifications were obtained in pre-frontal and frontal regions of brain. Also, wavelet, frequency and harmonic features were among the most discriminative features. ...
Mina Mirjalili and all who participated in EEG recording stage. ...
arXiv:1710.04462v1
fatcat:czdbvmcn6fbzndkscelzan5wxu
Advancing NLP with Cognitive Language Processing Signals
[article]
2019
arXiv
pre-print
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Specifically, we use gaze and EEG features to augment models of named entity recognition, relation classification, and sentiment analysis. ...
We analyze whether using such human features can show consistent improvement across tasks and data sources. ...
Additionally, we tested four EEG features, one for each combined frequency band: EEG t (i.e. the average values of theta1 and theta2), EEG a , EEG b , EEG g . ...
arXiv:1904.02682v1
fatcat:vtsqievrxvhrvl6sjsnetjkq54
Familiarity effects in EEG-based emotion recognition
2016
Brain Informatics
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Using both our experimental data and a sophisticated database (DEAP dataset), we investigated the effects of familiarity on brain activity based on EEG signals. ...
the performance of EEG-based emotion classification systems that adopt fractal dimension or power spectral density features and support vector machine, multilayer perceptron or C4.5 classifier. ...
produces higher power Familiarity effects in EEG-based emotion recognition 43 closely together at a specific frequency band. ...
doi:10.1007/s40708-016-0051-5
pmid:27747819
pmcid:PMC5319949
fatcat:elfia6au3bapdoookptui4awea
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing
[article]
2021
arXiv
pre-print
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We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. ...
Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. ...
In our setting, α = 0.05 and N = 18, accounting for the combination of the 3 embedding types and 6 EEG feature sets, namely broadband EEG; θ, α, β and γ frequency bands; and all four frequency bands jointly ...
arXiv:2102.08655v2
fatcat:el6ks6f45fgvhl2cvgoqzrczye
Using single-trial EEG to predict and analyze subsequent memory
2014
NeuroImage
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We show that it is possible to successfully predict subsequent memory performance based on single-trial EEG activity before and during item presentation in the study phase. ...
The overall accuracy across 18 subjects was 59.6% by combining pre- and during-stimulus information. ...
McDonnell Foundation grant to the Perceptual Expertise Network, and the KIBM (Kavli Institute of Brain and Mind) Innovative Research Grant. We would like to thank Dr. Marta Kutas and Dr. ...
doi:10.1016/j.neuroimage.2013.09.028
pmid:24064073
pmcid:PMC3874086
fatcat:3dgplllbdzfdnlvgjk7waqxniq
Emotion recognition based on EEG features in movie clips with channel selection
2017
Brain Informatics
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Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. ...
The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. ...
, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. ...
doi:10.1007/s40708-017-0069-3
pmid:28711988
pmcid:PMC5709281
fatcat:dlzwugw5wrd5fltrgqadyntehm
Data-driven spatial filtering for improved measurement of cortical tracking of multiple representations of speech
2019
Journal of Neural Engineering
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We aim to show that the EEG prediction from phoneme-related speech features is possible in Dutch. ...
Measurement of the cortical tracking of continuous speech from electroencephalography (EEG) recordings using a forward model is an important tool in auditory neuroscience. ...
Acknowledgements The authors would like to thank Giovanni Di Liberto for his helpful advice on the analysis, as well as Hugo Van hamme and Lien Decruy for helping with the phoneme segmentation. ...
doi:10.1088/1741-2552/ab3c92
pmid:31426053
fatcat:33rs3mf3ebfnliyhbr2ie4cgse
EEG decoding of spoken words in bilingual listeners: from words to language invariant semantic-conceptual representations
2015
Frontiers in Psychology
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Furthermore, employing two EEG feature selection approaches, we assessed the contribution of temporal and oscillatory EEG features to our classification results. ...
Both types of classification, showed a strong contribution of oscillations below 12 Hz, indicating the importance of low frequency oscillations in the neural representation of individual words and concepts ...
time and all EEG channels; and a timefrequency approach, relying on a combined selection of features using the temporal-windows approach and a moving filter-bandout procedure (4 Hz bands with an step ...
doi:10.3389/fpsyg.2015.00071
pmid:25705197
pmcid:PMC4319403
fatcat:dthnyajhqfbtdk2rgkunv3o3vu
Deep Learning for EEG-Based Preference Classification in Neuromarketing
2020
Applied Sciences
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Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features ...
The EEG-based preference recognition in neuromarketing was extensively reviewed. ...
Acknowledgments: The authors would like to thank the deanship of scientific research for funding and supporting this research through the initiative of DSR Graduate Students Research Support (GSR) at King ...
doi:10.3390/app10041525
fatcat:qciu4lro5fa3vjpdchealg5wd4
Decoding Neural Correlation of Language-Specific Imagined Speech using EEG Signals
[article]
2022
arXiv
pre-print
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The results showed the significant difference in the relative power spectral density between English and Chinese in specific frequency band groups. ...
However, studies in the EEG-based imagined speech domain still have some limitations due to high variability in spatial and temporal information and low signal-to-noise ratio. ...
The five frequency band groups were the delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-125 Hz) bands, which have been widely used in the analysis of EEG signals [7] ...
arXiv:2204.07362v1
fatcat:heqwvn26fvglzlufcfsylmnop4
A novel approach for detection of deception using Smoothed Pseudo Wigner-Ville Distribution (SPWVD)
2013
Journal of Biomedical Science and Engineering
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We found that combination of Time-Frequency and Classic features have better ability to achieve higher amount of accuracy. ...
Then, the best combinational feature vector is selected in order to improve classifier accuracy. Finally, Guilty and Innocent persons are classified by KNN and MLP. ...
(combinational form of Classic and Time-Frequency features), and also using the PCA method. ...
doi:10.4236/jbise.2013.61002
fatcat:f3r62c3iang77jeokwnxjfnxgq
Toward Exploiting EEG Input in a Reading Tutor
[chapter]
2011
Lecture Notes in Computer Science
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We also identify which EEG components appear sensitive to which lexical features. Better-than-chance performance shows promise for tutors to use EEG at school. ...
Using its signal from adults and children reading text and isolated words, both aloud and silently, we train and test classifiers to tell easy from hard sentences, and to distinguish among easy words, ...
We thank the students, educators, and LISTENers who helped generate, collect, and analyze our data, Sarah Laszlo for stimuli, and the reviewers for helpful comments. ...
doi:10.1007/978-3-642-21869-9_31
fatcat:pmv424m5lrdzdjaqlc3a4c3ubi
Remembered or Forgotten?—An EEG-Based Computational Prediction Approach
2016
PLoS ONE
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For ConvEEGNN, the average prediction accuracy was 72.07% by using EEG data from pre-stimulus and during-stimulus periods, outperforming other approaches. ...
In this paper, based on SMEs we propose a computational approach to predict memory performance of an event from EEG signals. ...
Using continuous wavelet transform (CWT), EEG of each trial was analyzed to extract time-frequency and spatial features, including 5 frequency bands at 4 processing stages and 3 scalp sites. ...
doi:10.1371/journal.pone.0167497
pmid:27973531
pmcid:PMC5156350
fatcat:toc6jemosvbhxl35u2cz7633k4
Tracking the Brain's Intrinsic Connectivity Networks in EEG
[article]
2021
bioRxiv
pre-print
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EEG-based features were then used to classify three cognitively-relevant brain networks with up to 75\% accuracy. ...
However, to realize this potential requires the ability to track brain networks using a more affordable imaging modality, such as Electroencephalography (EEG). ...
Frontal lobe EEG activity in the theta frequency band is also found to negatively correlate with the DMN [39] , and when combined with delta and alpha band powers, is capable of discriminating the DMN ...
doi:10.1101/2021.06.18.449078
fatcat:sxlf7fwqpfdlreeav3ysc4q66a
Comparison of Fractal Dimension and Wavelet Transform Methods in Classification of Stress State from EEG Signals
2022
International Journal of Computing and Digital Systems
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Consequently, the comparison between FD and wavelet transform has been conducted using electroencephalogram (EEG) signals recorded during the Stroop Colour Word Test (SCWT). ...
This research examines the implementation of the fractal dimension (FD) method as one of the features for stress state classification using brain signals. ...
Acknowledgment This research is partially supported by the Ministry of Higher Education Malaysia under the Higher Institution Centre of Excellence (HICoE) Scheme and grant for Presenting Academic Paper ...
doi:10.12785/ijcds/110115
fatcat:t72dyd2cvjalln33wfopspuava
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