{"title":"Using deep neural networks for natural saccade classification from electroencephalograms","authors":"Alexandre Drouin-Picaro, T. Falk","doi":"10.1109/EMBSISC.2016.7508606","DOIUrl":null,"url":null,"abstract":"This paper proposes a model to classify saccades from frontal (i.e., Fp1/Fp2) electroencephalography (EEG) data into up, down, left and right directions. The aim of the model is to provide brain-computer interfaces with improved cursor control without the need for a separate eye tracking device. To test the accuracy of the model \"in-the-wild,\" an EEG dataset with (uncontrolled) natural saccades was used, where subjects looked freely at images on a screen. EEG data was input to deep neural networks, namely a multi-layer perceptron and a convolutional neural network. As benchmarks, two systems were explored using features measured from the Fp1/2 EEG channels, as well as from the AF7/8, F7/8, FT7/8, and T7/8 channels. Experimental results show the proposed system achieving an accuracy of 72.92%, thus outperforming all benchmarks, which achieved accuracies of 51.80% and 50.72%, respectively.","PeriodicalId":361773,"journal":{"name":"2016 IEEE EMBS International Student Conference (ISC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE EMBS International Student Conference (ISC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMBSISC.2016.7508606","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
This paper proposes a model to classify saccades from frontal (i.e., Fp1/Fp2) electroencephalography (EEG) data into up, down, left and right directions. The aim of the model is to provide brain-computer interfaces with improved cursor control without the need for a separate eye tracking device. To test the accuracy of the model "in-the-wild," an EEG dataset with (uncontrolled) natural saccades was used, where subjects looked freely at images on a screen. EEG data was input to deep neural networks, namely a multi-layer perceptron and a convolutional neural network. As benchmarks, two systems were explored using features measured from the Fp1/2 EEG channels, as well as from the AF7/8, F7/8, FT7/8, and T7/8 channels. Experimental results show the proposed system achieving an accuracy of 72.92%, thus outperforming all benchmarks, which achieved accuracies of 51.80% and 50.72%, respectively.