Online multiclass EEG feature extraction and recognition using modified convolutional neural network method

Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accurac

Protecting privacy of users in brain-computer interface applications

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving(PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations

Emotional and cognitive responses to cultural heritage: a neuromarketing experiment using virtual reality in the tourist destination image model context

The research is driven to identify and measure, through electroencephalography the impact of cultural and architectural heritage emulated by virtual reality on the mind of a tourist, in the form of cognitive and emotional responses measured as brain waves, identifies, through electroencephalogram, the cognitive and the emotional responses by subjects immersed in cultural tourist destination virtual reality, takes as conceptual framework the tourist destination image model and focus on tourist destinations characterised by historical, cultural, and architectural heritage. A neuromarketing experiment is caried out in which the cognitive and affective responses can be observed and measured using alpha α and beta β brain bioelectric waves, the data obtained directly from the human brain is used to carry out an analysis using the partial least squares regression algorithm PLS. The results show that the cognitive and emotional response caused by the tourist destination cultural heritage through virtual reality in individuals, is positive

Spatiotemporal EEG Dynamics of Prospective Memory in Ageing and Mild Cognitive Impairment

Prospective memory (PM, the memory of future intentions) is one of the first complaints of those that develop dementia-related disease. Little is known about the neurophysiology of PM in ageing and those with mild cognitive impairment (MCI). By using a novel artificial neural network to investigate the spatial and temporal features of PM related brain activity, new insights can be uncovered. Young adults (n = 30), healthy older adults (n = 39) and older adults with MCI (n = 27) completed a working memory and two PM (perceptual, conceptual) tasks. Time-locked electroencephalographic potentials (ERPs) from 128-electrodes were analysed using a brain-inspired spiking neural network (SNN) architecture. Local and global connectivity from the SNNs was then evaluated. SNNs outperformed other machine learning methods in classification of brain activity between younger, older and older adults with MCI. SNNs trained using PM related brain activity had better classification accuracy than working memory related brain activity. In general, younger adults exhibited greater local cluster connectivity compared to both older adult groups. Older adults with MCI demonstrated decreased global connectivity in response to working memory and perceptual PM tasks but increased connectivity in the conceptual PM models relative to younger and healthy older adults. SNNs can provide a useful method for differentiating between those with and without MCI. Using brain activity related to PM in combination with SNNs may provide a sensitive biomarker for detecting cognitive decline. Cognitively demanding tasks may increase the amount connectivity in older adults with MCI as a means of compensation