Automated Classification of EEG Signals Using Component Analysis and Support Vector Machines

Epileptic seizures are characterized by abnormal electrical activity occurring in the brain. EEG records the seizures demonstrating changes in signal morphology. These signal characteristics, however, differ between patients as well as between different seizures in the same patient. Epilepsy is managed with anti-epileptic medications but in some extreme cases surgery might be necessary. Non-invasive surface electrode EEG measurement gives an estimate of the seizure onset but more invasive intra-cranial electrocorticogram (ECoG) are required at times for precise localization of the epileptogenic zone. The epileptogenic zone can be described as the cortical area targeted for resection to render the patient symptom free. Epileptologists use the “evolution” of aberrant signals for identifying epileptic seizures and the epileptogenic zone is identified by concentrating on the area contributing to the onset of seizure. This process is done by visually analyzing hours of ECoG data. The signal morphology during an epileptic seizure is not very different from abnormal discharges noticed in ECoG data thereby complicating signal analysis for the epileptologists. This thesis aims to classify the ECoG channel data as epileptic or non-epileptic using an automated machine learning algorithm called support vector machines (SVM). The data will be decomposed into various frequency bands identified by wavelet transform and will span the range of 0-30Hz. Statistical measures will be applied to these frequency bands to identify features that will subsequently be used to train SVM. This thesis will further investigate feature reduction using multivariate analysis methods to train the SVM and compare it to the performance of classification when all the features were used to train SVM. Results show that channel data classification using trained SVM that did not undergo feature reduction performed better with 98% sensitivity but needed more runtime than the SVM algorithms that was trained using reduced features. For high frequency analysis of frequencies between 60-500Hz, the results show the same sensitivity yet less specificity when compared to the classification using lower frequency range of 0-30Hz. The results seen in this thesis show that support vector machines classifiers can be trained to classify the data as epileptic or non-epileptic with good accuracy. Even though training the classifiers took almost two hours, it was still noticeably less than other machine learning algorithms such as artificial neural networks. The accuracy of this algorithm can be improved with changes to the data segment length, size of training matrix, accuracy of epileptic and nonepileptic data, and amount of data used for training

EEG During Motor Tasks in Stroke: The Effects of Remote Ischemic Conditioning and Fatigue on Brain Activity

This dissertation aimed to use electroencephalography (EEG) to identify the effects of fatigue and remote ischemic conditioning on brain activity. Lesions due to stroke directly or indirectly affect regions of the brain and the descending corticospinal pathways. Cortical reorganization and alternate descending neural pathways are used during recovery from stroke as compensation mechanisms for motor deficits. These mechanisms exacerbate the deficits by worsening the ability to terminate muscle activity, individuate muscles for fine motor control and minimize abnormal muscle synergy and coactivation patterns to conserve resources during movement. Even though imaging and muscle activation studies have documented the existence and impact of cortical reorganization and the use of alternate descending pathways, temporal changes in cortical activation during long motor tasks are not well understood. We expect that potential changes in cerebrovascular function and physiology of brain metabolism after stroke might impact the ability of the brain to produce extended activity. We used EEG for its high temporal resolution compared to other imaging modalities to document temporal changes in brain activity when people with stroke performed various motor tasks. We first documented the changes in activation during and at the end of a simple cued finger tap task between people with stroke and controls. We then pushed the neuromuscular system to its limits using a fatiguing contraction of the wrist to visualize changes in brain activation patterns after extended muscle contraction. Lastly, we tested a neurorehabilitation therapy protocol, remote ischemic conditioning (RIC), that has shown functional improvements in people with stroke to determine if cortical activation is changed during a complex, multijoint visuomotor task. The results show that cortical activation in people with stroke is divergent from controls. People with stroke continue brain activation at the end of a simple task but cannot increase activation at the end of a fatiguing task. RIC, however, increases activation during a multijoint elbow/shoulder task. This research has improved our understanding of brain activation during a simple task and in response to fatigue in people with stroke. The knowledge of cortical changes due to RIC demonstrates the therapy’s ability to “prime” the brain for neurorehabilitation, which might lead to better therapeutic outcomes post-rehabilitation in people with stroke

Synthesis of graphene by electrochemical exfoliation from petroleum coke for electrochemical energy storage application

The objective of the present work was to synthesize a graphene-like structure from petroleum coke (pet coke). Graphene is a potential alternative conducting material to replace traditional electrode materials such as indium tin oxide. The phosphoric acid was used to activate the pet coke in conditions where the coke to acid ratio is varied as 1:1, 1:2, 1:3, 1:4 and 1:5. The samples were kept at different temperatures in the furnace maintained in inert atmospheric conditions at 400, 500 and 600 °C for activation time intervals of 1, 2 and 3 h. The extent of activation of pet coke samples was characterized by their yield and iodine number. For the optimized conditions (600 °C, 3 h, 1:4 coke to acid ratio), the activated pet coke was moulded and taken as the anode for electrochemical exfoliation using platinum wire as cathode, and 0.3 M H2SO4 solution as electrolyte. The electrochemical exfoliation was carried out using DC power supply at 22 V for 8 h, and the obtained exfoliated product was analysed by surface-sensitive techniques (XRD, Raman and SEM). The specific capacitance values were measured using cyclic vol­tammetry in KOH, Na2SO4 and H2SO4 electrolytes. The highest specific capacitance value of 40 F g-1 for the scanning rate of 25 mV s-1 was obtained in 1 M H2SO4. It was confirmed that graphene-like structure produced from activated pet coke can be used as an alternate material for supercapacitor applications

BluBSIoT: Advancing Sustainability through Peer-to-Peer Cross-Ledgering in Social Internet of Things

The global emphasis on sustainability has stimulated the demand for state-of-the-art solutions that drive the green and blue economy. However, the exponentially growing data analysis remains constrained, leading to a substantial disparity between data supply and demand. This discrepancy primarily arises from data being isolated, inaccessible, and infrequently shared due to concerns regarding data governance and privacy breaches. To tackle these challenges, we propose the integration of Peer-to-Peer (P2P) cross-ledgering within the Social Internet of Things (SIoT) framework as a promising approach to advance cognitive sustainability through improved information sharing and storage. The P2P network configured at the base facilitates a decentralized and secure exchange of information among diverse stakeholders involved in promoting sustainability. By leveraging the immutability and authorized accessibility of blockchain, consortia nodes evaluate and segregate data suitable for on-chain, off-chain, or one-to-one transactions. This ensures the safeguarding of sensitive data while enabling seamless collaboration and sharing. The integration of ledger systems enables interoperability across multiple platforms, fostering smooth information exchange between entities engaged in green and blue economy initiatives

On-shell Supersymmetry and higher-spin amplitudes

We use on-shell Supersymmetry to constrain the three-point function of two massless particles and one massive particle in 3+1 dimensions. We use this information to write down the tree-level four-point function of massless particles for $\mathcal{N}=1$, $2$ and $4$ theories. In particular, we derive the expressions for four-photon/gluon amplitudes with massive higher spin exchange in theories with $\mathcal{N}=4$ Supersymmetry in 3+1 dimensions.Comment: 39 pages+ 4 appendice

Best Practices for Data Management in Citizen Science - An Indian Outlook

Citizen science has been in practice since the 1800s and is an important source of data for scientists and other applied users. It plays a vital role in democratizing science, providing equitable access to scientific participation and data, helps build the capacity of its participants, inculcates the spirit of scientific endeavor and discovery and sensitizes participants towards species and habitat conservation, creating a sense of stewardship towards nature. In recent years, citizen science, especially in biodiversity, has rapidly developed with the rising popularity of smartphones, and widespread access to the internet, leading to wider adoption globally. India has also witnessed a surge in the number of new citizen science projects being initiated and increased participation in these projects. With more proponents looking at initiating such projects, there is little documentation from an Indian perspective on setting up, collecting, managing, and maintaining biodiversity-focused citizen science projects, especially in a data-management context. We have attempted to fill this void by examining the best practices across the data life cycle of citizen science projects while keeping in mind sensitivities and scenarios in India. We hope this will prove to be an important reference for citizen science practitioners looking to better manage their data in their projects