LieWaves: dataset for lie detection based on EEG signals and wavelets.
ATAR
CNN
DWT
EEG
FFT
LSTM
Lie detection
OSW
Journal
Medical & biological engineering & computing
ISSN: 1741-0444
Titre abrégé: Med Biol Eng Comput
Pays: United States
ID NLM: 7704869
Informations de publication
Date de publication:
05 Feb 2024
05 Feb 2024
Historique:
received:
17
08
2023
accepted:
09
01
2024
medline:
5
2
2024
pubmed:
5
2
2024
entrez:
4
2
2024
Statut:
aheadofprint
Résumé
This study introduces an electroencephalography (EEG)-based dataset to analyze lie detection. Various analyses or detections can be performed using EEG signals. Lie detection using EEG data has recently become a significant topic. In every aspect of life, people find the need to tell lies to each other. While lies told daily may not have significant societal impacts, lie detection becomes crucial in legal, security, job interviews, or situations that could affect the community. This study aims to obtain EEG signals for lie detection, create a dataset, and analyze this dataset using signal processing techniques and deep learning methods. EEG signals were acquired from 27 individuals using a wearable EEG device called Emotiv Insight with 5 channels (AF3, T7, Pz, T8, AF4). Each person took part in two trials: one where they were honest and another where they were deceitful. During each experiment, participants evaluated beads they saw before the experiment and stole from them in front of a video clip. This study consisted of four stages. In the first stage, the LieWaves dataset was created with the EEG data obtained during these experiments. In the second stage, preprocessing was carried out. In this stage, the automatic and tunable artifact removal (ATAR) algorithm was applied to remove the artifacts from the EEG signals. Later, the overlapping sliding window (OSW) method was used for data augmentation. In the third stage, feature extraction was performed. To achieve this, EEG signals were analyzed by combining discrete wavelet transform (DWT) and fast Fourier transform (FFT) including statistical methods (SM). In the last stage, each obtained feature vector was classified separately using Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and CNNLSTM hybrid algorithms. At the study's conclusion, the most accurate result, achieving a 99.88% accuracy score, was produced using the LSTM and DWT techniques. With this study, a new data set was introduced to the literature, and it was aimed to eliminate the deficiencies in this field with this data set. Evaluation results obtained from the data set have shown that this data set can be effective in this field.
Identifiants
pubmed: 38311647
doi: 10.1007/s11517-024-03021-2
pii: 10.1007/s11517-024-03021-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. International Federation for Medical and Biological Engineering.
Références
Tatum WO, Husain AM, Benbadis SR, Kaplan PW (2008) Handbook of EEG interpretation. Demos Medical Publishing, United States of America
Coenen A, Fine E, Zayachkivska O (2014) Adolf Beck: a forgotten pioneer in electroencephalography. J Hist Neurosci 23(3):276. https://doi.org/10.1080/0964704X.2013.867600
doi: 10.1080/0964704X.2013.867600
pubmed: 24735457
Rodrigues JDASC, Filho PPR, Peixoto E, N AK, de Albuquerque VHC (2019) Classification of EEG signals to detect alcoholism using machine learning techniques. Pattern Recogn Lett 125:140–149. https://doi.org/10.1016/J.PATREC.2019.04.019
doi: 10.1016/J.PATREC.2019.04.019
Yargi V, Postalcioglu S (2021) Analysis of susceptibility to addiction using EEG signal with machine learning techniques. El-Cezerî J Sci Eng (ECJSE) 8(1):142–154. https://doi.org/10.31202/ecjse.787726
doi: 10.31202/ecjse.787726
Seal A, Reddy PPN, Chaithanya P, Meghana A, Jahnavi K, Krejcar O, Hudak R, Jiang YZ (2020) An EEG database and its initial benchmark emotion classification performance. Comput Math Methods Med 2020(8303465):14. https://doi.org/10.1155/2020/8303465
Alakus TB, Gonen M, Turkoglu I (2020) Database for an emotion recognition system based on EEG signals and various computer games – GAMEEMO. Biomed Signal Process Control 60:101951. https://doi.org/10.1016/J.BSPC.2020.101951
doi: 10.1016/J.BSPC.2020.101951
Joshi VM, Ghongade RB (2022) IDEA: intellect database for emotion analysis using EEG signal. J King Saud Univ - Comput Inform Sci 34(7):4433–4447. https://doi.org/10.1016/J.JKSUCI.2020.10.007
doi: 10.1016/J.JKSUCI.2020.10.007
Lajnef T, Chaibi S, Ruby P, Aguera PE, Eichenlaub JB, Samet M, Kachouri A, Jerbi K (2015) Learning machines and sleeping brains: automatic sleep stage classification using decision-tree multi-class support vector machines. J Neurosci Methods 250:94–105. https://doi.org/10.1016/J.JNEUMETH.2015.01.022
doi: 10.1016/J.JNEUMETH.2015.01.022
pubmed: 25629798
Guerrero-Mosquera C, Malanda A, Navia-Vazquez A (2012) EEG signal processing for epilepsy. Epilepsy - Histological Electroencephalographic Psychol Asp. https://doi.org/10.5772/31609
doi: 10.5772/31609
Balci F, Oralhan Z (2020) EEG Based Identification System Design via LSTM. Eur J Sci Technol 135–141. https://doi.org/10.31590/ejosat.779526
Abdulrahman SA, Roushdy M, M.Salem A-B (2020) Using K-nearest neighbors and support vector machine classifiers in personal identification based on EEG signals. Int J Comput Sci Inf Sec (IJCSIS) 18(5):29–37
Ong ZY, Saidatul A, Ibrahim Z (2018) Power spectral density analysis for human EEG-based biometric identification. 2018 Int Conf Comput Approach Smart Syst Des Appl ICASSDA 1–6. https://doi.org/10.1109/ICASSDA.2018.8477604
Cig H (2017) Device control with EEG signals. Master Thesis, Inonu University, Retrieved from https://tez.yok.gov.tr/UlusalTezMerkezi/
Ansari MF, Edla DR, Dodia S, Kuppili V (2019) Brain-computer interface for wheelchair control operations: an approach based on fast Fourier transform and on-line sequential extreme learning machine. Clin Epidemiol Glob Health 7(3):274–278. https://doi.org/10.1016/J.CEGH.2018.10.007
doi: 10.1016/J.CEGH.2018.10.007
Krishna G, Sai Kumar NVC, Tushal B, Gopal AV, Puripanda (2014) Micro-expression extraction for lie detection using Eulerian video (motion and color) magnification Submitted By. Master Thesis, Electrical Engineering, Blekinge Institute Of Technology, Retrieved from https://www.diva-portal.org/smash/get/diva2:830774/FULLTEXT01.pdf
Lisbona N (2022) High-tech lie detectors that can detect lies with cameras are being developed - BBC News Turkish, https://www.bbc.com/turkce/haberler-dunya-60198843 . Accessed 20 Mar 2023
Ergen M, Ülman YI (2012) Neuroscience, neurotechnology, lie detection and ethics. Acıbadem Univ J Health Sci 3:149–157
Krishnamurthy G, Majumder N, Poria S, Cambria E (2018) A deep learning approach for multimodal deception detection. 19th International conference on computational linguistics and intelligent text processing (CICLing). https://doi.org/10.48550/arXiv.1803.00344
Gupta V, Agarwal M, Arora M, Chakraborty T, Singh R, Vatsa M (2019) Bag-of-Lies: a multimodal dataset for deception detection. 2019 IEEE/CVF Conference on computer vision and pattern recognition workshops (CVPRW) 83–90. https://doi.org/10.1109/CVPRW.2019.00016
Karnati M, Seal A, Yazidi A, Krejcar O (2022) LieNet: a deep convolution neural network framework for detecting deception. IEEE Trans Cogn Dev Syst 14(3):971–984. https://doi.org/10.1109/TCDS.2021.3086011
doi: 10.1109/TCDS.2021.3086011
Gallardo-Antolín A, Montero JM (2021) Detecting deception from gaze and speech using a multimodal attention LSTM-based framework. Appl Sci 11(14):6393. https://doi.org/10.3390/app11146393
Javaid H, Dilawari A, Khan UG, Wajid B (2022) EEG guided multimodal lie detection with audio-visual cues. 2022 2nd International conference on artificial intelligence (ICAI) 71-78. https://doi.org/10.1109/ICAI55435.2022.9773469
Gao J, Tian H, Yang Y, Yu X, Li C, Rao N (2014) A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM. PLoS One 9(11):e109700. https://doi.org/10.1371/journal.pone.0109700
Baghel N, Singh D, Dutta MK, Burget R, Myska V (2020) Truth identification from EEG signal by using convolution neural network: lie detection. 2020 43rd International conference on telecommunications and signal processing (TSP) 550–553. https://doi.org/10.1109/TSP49548.2020.9163497
AlArfaj AA, Mahmoud HAH (2022) A deep learning model for EEG-based lie detection test using spatial and temporal aspects. Computers, Materials & Continua 73(3):5655–5669. https://doi.org/10.32604/CMC.2022.031135
doi: 10.32604/CMC.2022.031135
Farwell L, Donchin E (1991) The truth will out: interrogative polygraphy (“lie detection”) with event-related brain potentials. Psychophysiology 28:531–47. https://doi.org/10.1111/j.1469-8986.1991.tb01990.x
doi: 10.1111/j.1469-8986.1991.tb01990.x
pubmed: 1758929
Turnip A, FaizalAmri M, Suhendra MA, Kusumandari DE (2017) Lie detection based EEG-P300 signal classified by ANFIS method. Electron Comput Eng (JTEC) 9:107–110
Bablani A, Edla DR, Tripathi D, Venkatanareshbabu K (2018) Subject based deceit identification using empirical mode decomposition. Procedia Comput Sci 132:32–39. https://doi.org/10.1016/J.PROCS.2018.05.056
doi: 10.1016/J.PROCS.2018.05.056
Saini N, Bhardwaj S, Agarwal R (2019) Classification of EEG signals using hybrid combination of features for lie detection. Neural Comput Appl 32:3777–3787. https://doi.org/10.1007/s00521-019-04078-z
doi: 10.1007/s00521-019-04078-z
Haider SK, Jiang A, Jamshed MA, Pervaiz H, Mumtaz S (2018) Performance enhancement in P300 ERP single trial by machine learning adaptive denoising mechanism. IEEE Netw Lett 1(1):26–29. https://doi.org/10.1109/LNET.2018.2883859
doi: 10.1109/LNET.2018.2883859
Hasan KAM, Rahman M, Sharmin N (2019). Lie detection analyzing brain wave patterns using EEG headset. Degree of Bachelor of Science, Computer Science and Engineering, Daffodil International University. Retrieved from http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7940
Emotiv (2019) Insight user manual - technical specifications. https://emotiv.gitbook.io/insight-manual/v/insight-2019/introduction/technical-specifications . Accessed 3 Apr 2023
Emotiv (2023) EMOTIV insight 2.0–5 channel mobile brainwear. https://www.emotiv.com/product/emotiv-insight-5-channel-mobile-brainwear . Accessed 3 Apr 2023
Emotiv (2022) EmotivPro v3.0: notes on the data - DC offset. https://emotiv.gitbook.io/emotivpro-v3/notes-on-the-data/dc-offset . Accessed 3 Apr 2023
Bajaj N, Requena Carrión J, Bellotti F, Berta R, de Gloria A (2020) Automatic and tunable algorithm for EEG artifact removal using wavelet decomposition with applications in predictive modeling during auditory tasks. Biomed Signal Process Control 55. https://doi.org/10.1016/J.BSPC.2019.101624
Garg S, Patro RK, Behera S, Tigga NP, Pandey R (2021) An overlapping sliding window and combined features based emotion recognition system for EEG signals. Appl Comput Inform. https://doi.org/10.1108/ACI-05-2021-0130
Casciola AA, Carlucci SK, Kent BA, Punch AM, Muszynski MA, Zhou D, Kazemi A, Mirian MS, Valerio J, McKeown MJ, Nygaard HB (2021) A deep learning strategy for automatic sleep staging based on two-channel eeg headband data. Sensors 21(10):3316. https://doi.org/10.3390/s21103316
AL-Salman W, Li Y, Wen P (2019) K-complexes detection in EEG signals using fragment and frequency features coupled with an ensemble classification model. Neuroscience 422:119–133. https://doi.org/10.1016/J.NEUROSCIENCE.2019.10.034
doi: 10.1016/J.NEUROSCIENCE.2019.10.034
pubmed: 31682947
Abd A, Baykara M (2021) Feature extraction approach based on statistical methods and wavelet packet decomposition for emotion recognition using EEG signals. 2021 International conference on innovations in intelligent systems and applications (INISTA) 1–7. https://doi.org/10.1109/INISTA52262.2021.9548406
Cheong LC, Sudirman R, Hussin SS (2015) Feature extraction Of EGG signal using wavelet transform for autism classification. ARPN J Eng Appl Sci 10(2015):8533–8540. https://api.semanticscholar.org/CorpusID:38351340
Kumar N, Alam K, Siddiqi AH (2017) Wavelet transform for classification of EEG signal using SVM and ANN. Biomed Pharmacol J 10(4):2061–2069. https://doi.org/10.13005/BPJ/1328
doi: 10.13005/BPJ/1328
Amin HU, Mumtaz W, Subhani AR, Saad MNM, Malik AS (2017) Classification of EEG signals based on pattern recognition approach. Front Comput Neurosci 11:103. https://doi.org/10.3389/FNCOM.2017.00103/BIBTEX
doi: 10.3389/FNCOM.2017.00103/BIBTEX
pubmed: 29209190
pmcid: 5702353
Al-Fahoum AS, Al-Fraihat AA (2014) Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains. ISRN Neurosci 2014. https://doi.org/10.1155/2014/730218
Junxiu L, Guopei W, Yuling L, Senhui Q, Su Y, Wei L, Yifei B (2020) EEG-based emotion classification using a deep neural network and sparse autoencoder. Front Sys Neurosci 14(43). https://doi.org/10.3389/fnsys.2020.00043
Farhad Z, Retno W (2021) Emotion classification using 1D-CNN and RNN based on deap dataset. 10th International conference on natural language processing (NLP 2021) 363–378. https://doi.org/10.5121/csit.2021.112328
Mattioli F, Porcaro C, Baldassarre G (2021) A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface. J Neural Eng 18(6). https://doi.org/10.1088/1741-2552/ac4430
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
doi: 10.1162/neco.1997.9.8.1735
pubmed: 9377276
Erduran Avcı D, Yağbasan R (2008) Instructional strategies for the dominant use of the brain hemispheres. J Gazi Educ Fac 28(2):1–17
Unguren E (2016) The effect of neuroanatomical and neurochemical structure of the brain on personality and behavior. Int J Alanya Bus Fac 7(1):193–219
Sharma A, Vans E, Shigemizu D et al (2019) DeepInsight: a methodology to transform a non-image data to an image for Convolution Neural Network architecture. Sci Rep 9:11399. https://doi.org/10.1038/s41598-019-47765-6
doi: 10.1038/s41598-019-47765-6
pubmed: 31388036
pmcid: 6684600