4,127 Hits in 5.9 sec

Effects of Images with Different Levels of Familiarity on EEG [article]

Ali Saeedi, Ehsan Arbabi
2017 arXiv   pre-print
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]

Nora Hollenstein, Maria Barrett, Marius Troendle, Francesco Bigiolli, Nicolas Langer, Ce Zhang
2019 arXiv   pre-print
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

Nattapong Thammasan, Koichi Moriyama, Ken-ichi Fukui, Masayuki Numao
2016 Brain Informatics  
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]

Nora Hollenstein, Cedric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer, Ce Zhang
2021 arXiv   pre-print
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

Eunho Noh, Grit Herzmann, Tim Curran, Virginia R. de Sa
2014 NeuroImage  
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

Mehmet Siraç Özerdem, Hasan Polat
2017 Brain Informatics  
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

Damien Lesenfants, Jonas Vanthornhout, Eline Verschueren, Tom Francart
2019 Journal of Neural Engineering  
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

João M. Correia, Bernadette Jansma, Lars Hausfeld, Sanne Kikkert, Milene Bonte
2015 Frontiers in Psychology  
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

Mashael ALdayel, Mourad Ykhlef, Abeer Al-Nafjan
2020 Applied Sciences  
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]

Keon-Woo Lee and Dae-Hyeok Lee and Sung-Jin Kim and Seong-Whan Lee
2022 arXiv   pre-print
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)

Elias Ebrahimzadeh, Seyed Mohammad Alavi, Ahmad Bijar, Alireza Pakkhesal
2013 Journal of Biomedical Science and Engineering  
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]

Jack Mostow, Kai-min Chang, Jessica Nelson
2011 Lecture Notes in Computer Science  
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

Xuyun Sun, Cunle Qian, Zhongqin Chen, Zhaohui Wu, Benyan Luo, Gang Pan, Lutz Jaencke
2016 PLoS ONE  
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]

Saurabh Bhaskar Shaw, Margaret C. McKinnon, Jennifer J Heisz, Amabilis H. Harrison, John F. Connolly, Suzanna Becker
2021 bioRxiv   pre-print
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

Fatimah Abdul Hamid, Norshakila Haris, Mohamad Naufal Mohamad Saad
2022 International Journal of Computing and Digital Systems  
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
« Previous Showing results 1 — 15 out of 4,127 results