Brain signal recognition using deep learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology serves as the best available non-invasive solution for extracting signals from the brain. On the other hand, speech is the primary means of communication, but for patients suffering from locked-in syndrome, there is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a thought-to-speech BCI system. This research aims to investigate methods for the recognition of imagined speech from EEG signals using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety of issues have been solved. These include 1) proposing new feature extraction and classification framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep learning methods were used to analyze EEG signals. For recognition of imagined speech from EEG signals, a new EEG database was collected while the participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech). Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined words from EEG signals. Further, a deep learning framework using the convolutional network and attention mechanism was implemented for learning features in the spatial, temporal, and spectral domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments show that deep learning algorithms are ideal for imagined speech recognition from EEG signals due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes of imagined words from EEG signals were also recognized using a multi-channel convolution network framework. This method was extended to a multi-level recognition system for multi-class classification of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in comparison to previous work. In order to investigate the difference between imagined speech with verbal speech and visual imagery from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies that result in most discrimination between the different cognitive tasks were also explored. A framework was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals. The proposed method used the K-means clustering algorithm to find the best electrode combination and convolutional-attention network for feature extraction and classification. The proposed method achieved a high recognition rate of 82.9% and 77.7%. The results in this research suggest that a communication based BCI system can be designed using deep learning methods. Further, this work add knowledge to the existing work in the field of communication based BCI system

Expatriate Adolescents’ Resilience: Risk and Protective Factors in the Third Culture Context

Expatriate children and adolescents typically spend several of their formative years moving from country to country, frequently having to adapt to new cultures, making new friends, and fit into new school systems. It has been established in literature that such frequent changes may cause increased and prolonged risk of developing internalizing behavior problems such as depression and anxiety. However, little is still known regarding which protective factors serve as buffer towards the increased risk within the expatriate demographic. This study examined risk and protective factors among a group of expatriates, adolescents, and their parents, originating from 21 countries on five continents. Adolescent resilience was established through measuring risk and protective factors within three domains (i) individual, (ii) family, and (iii) school/community. In particular, the results indicated that adolescents’ sense of coherence, positive family climate, and satisfaction with school and friends, each predicted resilience significantly above other demographic factors. Interestingly, higher number of international moves did not predict adolescents’ resilience. The results imply that a coherent identity, high self-esteem, sense of “Third Cultural” group belonging, paired with a robust family environment, would promote resilience in the expatriate population. This may in turn serve as a buffer towards the negative effects caused by a stressful, transient upbringing

Conservation of microsatellite regions across legume genera increases marker repertoire in pigeonpea

Abstract Microsatellite markers from chickpea, common bean, fieldpea and lentil were studied for their transferability and ability to reveal polymorphism in pigeonpea with an objective to use them in linkage map construction and tagging of agronomically important traits. Out of total one hundred and sixty three genic and genomic markers from four legume genera screened on six pigeonpea genotypes, 58 were found to be transferable in pigeonpea. Maximum transferability (47%) was shown by markers from common bean, followed by lentil, fieldpea and chickpea. The average polymorphism information content value with genic and genomic markers was found to be 0.60 to 0.50 respectively. These transferable markers will add to the pool of available markers for genotyping and mapping of important traits in Cajanus. This study also demonstrated that genic markers are not only transferable across genera but also are at par with genomic markers in detecting polymorphism

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Not AvailableTo study the conservation of microsatellite regions, a set of 137 microsatellite markers developed from Phaseolus, Cajanus, Lens and Cicer genera of Leguminosae family were tested for their transferability across 16 genotypes of Phaseolus belonging to diverse collections from South America and Asia. Considerable transferability was observed with markers derived from Cajanus (60%), Lens (46%) and Cicer (28%). Of the total 122 loci were amplified, 82 cross-species polymorphic amplicons were obtained. Maximum number of alleles per marker was six (Cicer markers). Polymorphism information content values ranged from 0.12 to 0.96 with Cajanus, 0.13 to 0.74 with Lens and 0.30 to 0.93 with Cicer markers. Unweighted pair group method employing arithmetic averages cluster analysis of Phaseolus genotypes showed clear demarcation between the commercial cultivars falling in separate cluster with respect to their seed size and maturity. Transferability of genomic SSRs was different from that of expressed sequence tag-derived genic microsatellite markers.Not Availabl

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Abstracts of 1st International Conference on Machine Intelligence and System Sciences

This book contains the abstracts of the papers presented at the International Conference on Machine Intelligence and System Sciences (MISS-2021) Organized by the Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea, held on 1–2 November 2021. This conference was intended to enable researchers to build connections between different digital technologies based on Machine Intelligence, Image Processing, and the Internet of Things (IoT). Conference Title: 1st International Conference on Machine Intelligence and System SciencesConference Acronym: MISS-2021Conference Date: 1–2 November 2021Conference Location: Techno College of Engineering Agartala, Tripura(w), IndiaConference Organizer: Techno College of Engineering, Agartala, Tripura, India & Tongmyong University, Busan, South Korea