Engellilerin Taleplerinin Yapay Zeka Yöntemleriyle Belirlenmesi

Yıl 2021, Cilt: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Sayı: Special, 226 - 241, 20.10.2021

Mucahit Karaduman , Ali Karci

https://doi.org/10.53070/bbd.990485

Öz

Beyin aktiviteleri ve uzaktan kontrol üzerinde çalışma yapılan güncel konular arasında önemli yer tutmaktadırlar. Beyin işlevleri sırasında ortaya çıkan sinyallerin analizi elektroansefalografi (EEG) olmaktadır. Sinyallerin düşünsel, görsel ve hareket sonucu oluşmak gibi şekilleri mevcuttur. Özellikle görsel uyaran ile oluşan EEG sinyalleri bu çalışmanın alanına girmektedir. Bu çalışmada görsel şekillere bakan bir kişide oluşan EEG sinyallerinin sınıflandırılması üzerine araştırmalar yapılmaktadır. Bu amaçla EEG sinyalleri kaydedilmiş ve filtrelenerek gürültüden arındırılmıştır. Daha sonra sinyallerden öznitelikler çıkarılmıştır. Bu çalışmada özellikle ortalama, medyan, standart sapma ve Entropi gibi birçok çalışmada kullanılan özniteliklerin yanı sıra Moment 5 özniteliği de kullanılmıştır. Daha sonra Destek vektör makinesi (SVM), k en yakın komşu(KNN), ve karar ağacı(DT) algoritmaları kullanarak sınıflandırma yapılmıştır. Sınıflandırma yapılırken 4 farklı sınıf kullanılmıştır. Bu sınıflara kare, daire, üçgen ve yıldız isimleri verilmiştir. Sonuç olarak SVM ve KNN algoritmaları 99.99% doğruluk oranı ile bakılan şeklin hangisi olduğunu elde etmişlerdir. Böylece görülüyor ki beyin bakılan şeklin yapısına göre farklı sinyaller üretmektedir. Bu durum hastaların sadece bakarak veya düşünerek isteklerini bildirme imkanı tanımak için bir yöntem olarak kullanılabileceğini göstermektedir.

Anahtar Kelimeler

EEG, beyin sinyali, görsel uyaran, şekil tahmini

Destekleyen Kurum

İnönü Üniversitesi

Proje Numarası

FBA-2019-1664

Determining the Demands of Disabled People by Artificial Intelligence Methods

Öz

Analysis of brain activities and remote control are among the current issues that are being studied. Analysis of signals arising during brain functions is electroencephalography (EEG). EEG signals have intellectual, visual stimulation, and motion resultant forms. Especially, EEG signals generated by visual stimulus are within the scope of this study. In this study, research was carried out on the classification of EEG signals formed in a person looking at visual figures. For these studies, first of all, EEG signals from the brain were recorded with images and filtered to remove noise. Then, the features were extracted from the signals. In this study, Moment 5 feature was also used in addition to the features used in many studies such as mean, median, standard deviation and entropy. Then, classification was made using Support Vector Machine (SVM), k Nearest Neighbor (KNN), and Decision Tree (DT) algorithms. Classification was made for 4 different visual shapes used, since these shapes are square, circle, triangle, and star, and the same categorical names were used in the classification stage. As a result of the classification of EEG signals; SVM and KNN algorithms have determined which shape is viewed with 99.99% accuracy. These results show that different signals are produced in the brain according to the structure of the shape viewed. This situation shows that it can be used as a method to give patients the opportunity to express their requests just by looking or thinking.

Anahtar Kelimeler

EEG, brain signal, visual stimulus, shape prediction

Proje Numarası

FBA-2019-1664

Kaynakça

• Abdulla S, Diykh M, Laft RL, Saleh K, Deo RC (2019) Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm. Expert Syst Appl 138:112790 . https://doi.org/10.1016/j.eswa.2019.07.007
• Acharya UR, Sree SV, Chattopadhyay S, Suri JS (2012) Automated diagnosis of normal and alcoholic EEG signals. Int J Neural Syst 22: . https://doi.org/10.1142/S0129065712500116
• Adak MF, Yurtay N (2017) Gini Algoritmasını Kullanarak Karar Ağacı Oluşturmayı Sağlayan Bir Yazılımın Geliştirilmesi Developing a Software Which Provides Creating Decision Trees by Using Gini Algorithm. 1–6
• Chatterjee R, Maitra T, Hafizul Islam SK, Hassan MM, Alamri A, Fortino G (2019) A novel machine learning based feature selection for motor imagery EEG signal classification in Internet of medical things environment. Futur Gener Comput Syst 98:419–434 . https://doi.org/10.1016/j.future.2019.01.048
• Chen X, Zhao B, Wang Y, Xu S, Gao X (2018) Control of a 7-DOF Robotic Arm System with an SSVEP-Based BCI. Int J Neural Syst 28: . https://doi.org/10.1142/S0129065718500181
• Chowdhury MSN, Dutta A, Robison MK, Blais C, Brewer GA, Bliss DW (2020) Deep neural network for visual stimulus-based reaction time estimation using the periodogram of single-trial eeg. Sensors (Switzerland) 20:1–20 . https://doi.org/10.3390/s20216090
• Çomak E, Arslan A, Türkoǧlu I (2007) A decision support system based on support vector machines for diagnosis of the heart valve diseases. Comput Biol Med 37:21–27 . https://doi.org/10.1016/j.compbiomed.2005.11.002
• Diykh M, Li Y, Abdulla S (2020) EEG sleep stages identification based on weighted undirected complex networks. Comput Methods Programs Biomed 184: . https://doi.org/10.1016/j.cmpb.2019.105116
• Ellerman D (2013) An introduction to logical entropy and its relation to shannon entropy. Int J Semant Comput 7:121–145 . https://doi.org/10.1142/S1793351X13400059
• Er MB, Çiğ H, Aydilek İB (2021) A new approach to recognition of human emotions using brain signals and music stimuli. Appl Acoust 175: . https://doi.org/10.1016/j.apacoust.2020.107840
• Faiz MZ Al, Al-Hamadani AA (2019) Online brain computer interface based five classes EEG to control humanoid robotic hand. 2019 42nd Int Conf Telecommun Signal Process TSP 2019 406–410 . https://doi.org/10.1109/TSP.2019.8769072
• Hu L, Zhang Z (2019) EEG Signal Processing and Feature Extraction. Springer Singapore, Singapore
• Jiang X, Bian G Bin, Tian Z (2019) Removal of artifacts from EEG signals: A review. Sensors (Switzerland) 19:1–18 . https://doi.org/10.3390/s19050987
• Kapeller C, Hintermuller C, Abu-Alqumsan M, Pruckl R, Peer A, Guger C (2013) A BCI using VEP for continuous control of a mobile robot. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 5254–5257 . https://doi.org/10.1109/EMBC.2013.6610734
• Karaduman M, Karcı A (2020) Controlling Vehicles Using EEG Signal And Eye-Arm Collaboration. In: ISERD 180th International Conference. Macca, pp 19–24
• Karcı A (2016) Fractional order entropy: New perspectives. Optik (Stuttg) 127:9172–9177 . https://doi.org/10.1016/j.ijleo.2016.06.119
• Kilicoglu T. Astroistatistik, https://acikders.ankara.edu.tr/pluginfile.php/107751/mod_resource/content/0/Astroistatistik_Konu_05_Momentler_Carpiklik_ve_Basiklik.pdf (15.12.2020)
• Kumar S, Sharma A, Tsunoda T (2017) An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information. BMC Bioinformatics 18: . https://doi.org/10.1186/s12859-017-1964-6
• Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F (2018) A review of classification algorithms for EEG-based brain-computer interfaces: A 10 year update. J Neural Eng 15: . https://doi.org/10.1088/1741-2552/aab2f2
• Martišius I, Damaševičius R (2016) A prototype SSVEP based real time BCI gaming system. Comput Intell Neurosci 2016: . https://doi.org/10.1155/2016/3861425
• Mousavi Z, Yousefi Rezaii T, Sheykhivand S, Farzamnia A, Razavi SN (2019) Deep convolutional neural network for classification of sleep stages from single-channel EEG signals. J Neurosci Methods 324: . https://doi.org/10.1016/j.jneumeth.2019.108312
• Murugappan M, Juhari MRBM, Nagarajan R, Yaacob S (2009) An investigation on visual and audiovisual stimulus based emotion recognition using EEG. Int J Med Eng Inform 1:342–356 . https://doi.org/10.1504/IJMEI.2009.022645
• Namazi H, Kulish V V., Akrami A (2016) The analysis of the influence of fractal structure of stimuli on fractal dynamics in fixational eye movements and EEG signal. Sci Rep 6:1–8 . https://doi.org/10.1038/srep26639
• San-Segundo R, Gil-Martín M, D’Haro-Enríquez LF, Pardo JM (2019) Classification of epileptic EEG recordings using signal transforms and convolutional neural networks. Comput Biol Med 109:148–158 . https://doi.org/10.1016/j.compbiomed.2019.04.031
• Sazgar M, Young MG (2019) Absolute Epilepsy and EEG Rotation Review: Essentials for trainees
• Shao L, Zhang L, Belkacem AN, Zhang Y, Chen X, Li J, Liu H, Minati L (2020) EEG-Controlled Wall-Crawling Cleaning Robot Using SSVEP-Based Brain-Computer Interface. J Healthc Eng 2020: . https://doi.org/10.1155/2020/6968713
• Singh A, Pusarla N, Sharma S, Kumar T (2020) CNN-based Epilepsy detection using image like features of EEG signals. Int Conf Electr Electron Eng ICE3 2020 280–284 . https://doi.org/10.1109/ICE348803.2020.9122874
• Sreeja SR, Samanta D (2019) Classification of multiclass motor imagery EEG signal using sparsity approach. Neurocomputing 368:133–145 . https://doi.org/10.1016/j.neucom.2019.08.037
• Tuncer T, Dogan S, Akbal E (2019) A novel local senary pattern based epilepsy diagnosis system using EEG signals. Australas Phys Eng Sci Med 42:939–948 . https://doi.org/10.1007/s13246-019-00794-x
• Zgallai W, Brown JT, Ibrahim A, Mahmood F, Mohammad K, Khalfan M, Mohammed M, Salem M, Hamood N (2019) Deep Learning AI Application to an EEG driven BCI Smart Wheelchair. 2019 Adv Sci Eng Technol Int Conf ASET 2019 14–18 . https://doi.org/10.1109/ICASET.2019.8714373
• Zhang D, Yao L, Chen K, Monaghan J (2019) A Convolutional Recurrent Attention Model for Subject-Independent EEG Signal Analysis. IEEE Signal Process Lett 26:715–719 . https://doi.org/10.1109/LSP.2019.2906824
• Zheng X, Chen W, You Y, Jiang Y, Li M, Zhang T (2020) Ensemble deep learning for automated visual classification using EEG signals. Pattern Recognit 102:107147 . https://doi.org/10.1016/j.patcog.2019.107147
• Zhou Y, He S, Huang Q, Li Y (2020) A Hybrid Asynchronous Brain-Computer Interface Combining SSVEP and EOG Signals. IEEE Trans Biomed Eng 67:2881–2892 . https://doi.org/10.1109/TBME.2020.2972747