{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T11:57:05Z","timestamp":1782302225800,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN\u2019s trained weights.<\/jats:p>","DOI":"10.3390\/s23239351","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T06:06:23Z","timestamp":1700719583000},"page":"9351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Nastaran","family":"Khaleghi","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaghayegh","family":"Hashemi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shahid Beheshti University, Tehran 19839-69411, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sevda Zafarmandi","family":"Ardabili","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Southern Methodist University, Dallas, TX 75205, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2275-8133","authenticated-orcid":false,"given":"Sobhan","family":"Sheykhivand","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, University of Bonab, Bonab 55517-61167, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8258-0437","authenticated-orcid":false,"given":"Sebelan","family":"Danishvar","sequence":"additional","affiliation":[{"name":"College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"056013","DOI":"10.1088\/1741-2560\/9\/5\/056013","article-title":"Combining features from ERP components in single-trial EEG for discriminating four-category visual objects","volume":"9","author":"Wang","year":"2012","journal-title":"J. Neural Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/S0896-6273(01)00424-X","article-title":"The neural basis of perceptual learning","volume":"31","author":"Gilbert","year":"2001","journal-title":"Neuron"},{"key":"ref_3","unstructured":"Shenoy, P., and Tan, D.S. (May, January 26). Human-aided computing: Utilizing implicit human processing to classify images. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Toronto, ON, Canada."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"031005","DOI":"10.1088\/1741-2552\/aab2f2","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces: A 10 year update","volume":"15","author":"Lotte","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"124154","DOI":"10.1109\/ACCESS.2022.3223354","article-title":"Method for Identification of Multiple Low-Voltage Signal Sources Transmitted Through a Conductive Medium","volume":"10","author":"Namazifard","year":"2022","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Namazifard, S., and Subbarao, K. (2023). Multiple dipole source position and orientation estimation using non-invasive EEG-like signals. Sensors, 23.","DOI":"10.3390\/s23052855"},{"key":"ref_7","first-page":"75","article-title":"Recognition COVID-19 cases using deep type-2 fuzzy neural networks based on chest X-ray image","volume":"14","author":"Sabahi","year":"2023","journal-title":"Comput. Intell. Electr. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"063007","DOI":"10.1115\/1.4052167","article-title":"Retrograde gas condensate reservoirs: Reliable estimation of dew point pressure by the hybrid neuro-fuzzy connectionist paradigm","volume":"144","author":"Bagherzadeh","year":"2022","journal-title":"J. Energy Resour. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Baradaran, F., Farzan, A., Danishvar, S., and Sheykhivand, S. (2023). Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals. Electronics, 12.","DOI":"10.3390\/electronics12102232"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"A236","DOI":"10.1093\/sleep\/zsad077.0537","article-title":"0537 Incident Hypertension Prediction in Obstructive Sleep Apnea using Machine Learning","volume":"46","author":"Milani","year":"2023","journal-title":"Sleep"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neucom.2021.02.052","article-title":"Autonomous deep feature extraction based method for epileptic EEG brain seizure classification","volume":"444","author":"Woodbright","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ak, A., Topuz, V., and Midi, I. (2022). Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator. Biomed. Signal Process. Control, 72.","DOI":"10.1016\/j.bspc.2021.103295"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kwak, N.-S., M\u00fcller, K.-R., and Lee, S.-W. (2017). A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0172578"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Spampinato, C., Palazzo, S., Kavasidis, I., Giordano, D., Souly, N., and Shah, M. (2017, January 21\u201326). Deep learning human mind for automated visual classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.479"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-0967-9","article-title":"EEG-based image classification via a region-level stacked bi-directional deep learning framework","volume":"19","author":"Fares","year":"2019","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.neucom.2019.12.076","article-title":"Reading into the mind\u2019s eye: Boosting automatic visual recognition with EEG signals","volume":"386","author":"Cudlenco","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mathur, N., Gupta, A., Jaswal, S., and Verma, R. (2021). Deep learning helps EEG signals predict different stages of visual processing in the human brain. Biomed. Signal Process. Control, 70.","DOI":"10.1016\/j.bspc.2021.102996"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ghosh, L., Dewan, D., Chowdhury, A., and Konar, A. (2021). Exploration of face-perceptual ability by EEG induced deep learning algorithm. Biomed. Signal Process. Control, 66.","DOI":"10.1016\/j.bspc.2020.102368"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1167\/10.7.1394","article-title":"Rapid natural image identification based on EEG data and Global Scene Statistics","volume":"10","author":"Ghebreab","year":"2010","journal-title":"J. Vis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1038\/nature06713","article-title":"Identifying natural images from human brain activity","volume":"452","author":"Kay","year":"2008","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1038\/nn1445","article-title":"Predicting the orientation of invisible stimuli from activity in human primary visual cortex","volume":"8","author":"Haynes","year":"2005","journal-title":"Nat. Neurosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1016\/j.neuroimage.2006.06.062","article-title":"Inverse retinotopy: Inferring the visual content of images from brain activation patterns","volume":"33","author":"Thirion","year":"2006","journal-title":"Neuroimage"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"13992","DOI":"10.1523\/JNEUROSCI.3577-09.2009","article-title":"Decoding and reconstructing color from responses in human visual cortex","volume":"29","author":"Brouwer","year":"2009","journal-title":"J. Neurosci."},{"key":"ref_24","unstructured":"Koch, C., and Ullman, S. (1987). Matters of Intelligence, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","unstructured":"Achanta, R., Estrada, F., Wils, P., and S\u00fcsstrunk, S. (2008, January 12\u201315). Salient region detection and segmentation. Proceedings of the International Conference on Computer Vision Systems, Santorini, Greece."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ma, Y.-F., and Zhang, H.-J. (2003, January 4\u20136). Contrast-based image attention analysis by using fuzzy growing. Proceedings of the eleventh ACM International Conference on Multimedia, Berkeley, CA, USA.","DOI":"10.1145\/957013.957094"},{"key":"ref_28","unstructured":"Hu, Y., Rajan, D., and Chia, L.-T. (December, January 28). Robust subspace analysis for detecting visual attention regions in images. Proceedings of the 13th annual ACM International Conference on Multimedia, Singapore."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1016\/j.patcog.2009.04.021","article-title":"A simple method for detecting salient regions","volume":"42","author":"Rosin","year":"2009","journal-title":"Pattern Recognit."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Valenti, R., Sebe, N., and Gevers, T. (October, January 29). Image saliency by isocentric curvedness and color. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan.","DOI":"10.1109\/ICCV.2009.5459240"},{"key":"ref_31","first-page":"82127","article-title":"Cognitive psychology. Appleton-Century-Crofts. [aJRH] Newell, A. (1982) The knowledge level","volume":"18","author":"Neisser","year":"1967","journal-title":"Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Sclaroff, S. (2013, January 1\u20138). Saliency detection: A boolean map approach. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.26"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3312","DOI":"10.1016\/j.apm.2011.10.029","article-title":"Adaptive Q\u2013S synchronization between coupled chaotic systems with stochastic perturbation and delay","volume":"36","author":"Zhao","year":"2012","journal-title":"Appl. Math. Model."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1167\/14.1.28","article-title":"Predicting human gaze beyond pixels","volume":"14","author":"Xu","year":"2014","journal-title":"J. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1109\/TPAMI.2016.2547384","article-title":"Top-down visual saliency via joint CRF and dictionary learning","volume":"39","author":"Yang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1007\/s11263-015-0822-0","article-title":"Supercnn: A superpixelwise convolutional neural network for salient object detection","volume":"115","author":"He","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_37","unstructured":"Li, G., and Yu, Y. (2015, January 7\u201312). Visual saliency based on multiscale deep features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, X., Shen, C., Boix, X., and Zhao, Q. (2015, January 7\u201313). Salicon: Reducing the semantic gap in saliency prediction by adapting deep neural networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.38"},{"key":"ref_39","unstructured":"Pan, J., Sayrol, E., Giro-i-Nieto, X., McGuinness, K., and O\u2019Connor, N.E. (July, January 26). Shallow and deep convolutional networks for saliency prediction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e13267","DOI":"10.1111\/psyp.13267","article-title":"Presaccadic EEG activity predicts visual saliency in free-viewing contour integration","volume":"55","author":"Meghanathan","year":"2018","journal-title":"Psychophysiology"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neunet.2018.04.013","article-title":"Characterization of electroencephalography signals for estimating saliency features in videos","volume":"105","author":"Liang","year":"2018","journal-title":"Neural Netw."},{"key":"ref_42","unstructured":"Tavakoli, H.R., and Laaksonen, J. (2016, January 20\u201324). Bottom-up fixation prediction using unsupervised hierarchical models. Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, W., He, H., Xian, B., Zeng, M., Zhou, H., Niu, L., and Chen, G. (2017). Object extraction in cluttered environments via a P300-based IFCE. Comput. Intell. Neurosci., 2017.","DOI":"10.1155\/2017\/5468208"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1109\/TPAMI.2020.2995909","article-title":"Decoding brain representations by multimodal learning of neural activity and visual features","volume":"43","author":"Palazzo","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Khaleghi, N., Rezaii, T.Y., Beheshti, S., Meshgini, S., Sheykhivand, S., and Danishvar, S. (2022). Visual Saliency and Image Reconstruction from EEG Signals via an Effective Geometric Deep Network-Based Generative Adversarial Network. Electronics, 11.","DOI":"10.3390\/electronics11213637"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Khaleghi, N., Rezaii, T.Y., Beheshti, S., and Meshgini, S. (2023). Developing an efficient functional connectivity-based geometric deep network for automatic EEG-based visual decoding. Biomed. Signal Process. Control, 80.","DOI":"10.1016\/j.bspc.2022.104221"},{"key":"ref_47","unstructured":"Vivancos, D., and Cuesta, F. (2022). MindBigData 2022 A Large Dataset of Brain Signals. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1113\/jphysiol.1959.sp006308","article-title":"Receptive fields of single neurones in the cat\u2019s striate cortex","volume":"148","author":"Hubel","year":"1959","journal-title":"J. Physiol."},{"key":"ref_49","unstructured":"Fukushima, K. (1979, January 20\u201323). Self-organization of a neural network which gives position-invariant response. Proceedings of the 6th International Joint Conference on Artificial Intelligence, Tokyo, Japan."},{"key":"ref_50","unstructured":"LeCun, Y. (2023, November 13). The MNIST Database of Handwritten Digits. Available online: http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/TPAMI.2018.2815601","article-title":"What do different evaluation metrics tell us about saliency models?","volume":"41","author":"Bylinskii","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","unstructured":"Gu, K., Zhai, G., Yang, X., Zhang, W., and Liu, M. (2013, January 15\u201319). Structural similarity weighting for image quality assessment. Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), San Jose, CA, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:28:16Z","timestamp":1760131696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/23\/9351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":53,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23239351"],"URL":"https:\/\/doi.org\/10.3390\/s23239351","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,23]]}}}