A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

A survey on methods and challenges in EEG based authentication

In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi‐supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi‐supervised learning on EEG data analytics and discussing the importance of semi‐supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.

Deep learning for EEG data analytics: A survey

With the rapid development of brain–computer interfaces (BCI), the number of applications that use BCI technology is increasingly thick and fast. Prediction of age and gender of a person through EEG analysis is a new application of BCI that has been proposed in this paper. An industry standard EEG recording device has been used to record cerebral activities of 60 subjects (both male and female) in relaxed position with closed eyes. Deep BLSTM-LSTM network has been used to construct a hybrid learning framework for the aforementioned analysis. Accuracy of 93.7% and 97.5% have been recorded for age and gender classification problems respectively. These values are better than the state-of-the-art methods. Our analysis also reveals that the beta band frequencies are better in predicting the age and gender as compared to other frequency bands of the EEG signals. The proposed method has several applications, including biometric, health-care, entertainment, and targeted advertisements.

EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model

Detecting abnormal electroencephalograms using deep convolutional networks.

Brain fog, also known as confusion, is one of the main reasons of the low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in human's mind in real time is a challenging and important task which can be applied to online education, driver fatigue detection and so on. In this paper, we applied Bidirectional LSTM Recurrent Neural Networks to classify students' confusions. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activities.

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Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks

This article is written in recognition of W. Bledsoe, who with Browning, introduced the N-tuple subspace classifier in 1959. This 1959 article was the first article to introduce subspace classifiers and the sum rule to combine the outputs of the classifiers. A mathematical notation is given to easily express in a precise and unambiguous way everything going on in the N-tuple subspace classifier. Extensions of the N-tuple method are discussed using a generalized product expression and we relate the generalization to graphical models. We discuss the sum rule, the product rule, and the plurality voting rule for combining the scores of the subspace classifiers. We selected a representative sample of papers that the 1959 N-tuple subspace classifier inspired. Some of the papers introduced specialized improvements. Many of the papers showed the value of the N-tuple subspace classifier in all kinds of applications and compared the results of one or more varieties of the N-tuple subspace classifier with other state-of-the-art classifiers. Their experiments showed that the N-tuple subspace classifier was competitive with the state-of-the-art classifiers and often had a higher accuracy. Finally, we highlight some papers that describe experiments of the N-tuple subspace classifier executing in a quantum computer.

Ahmet Cem Yuksel

Papers

Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks

The N-Tuple Subspace Classifier: Extensions and Survey