PyNoetic’s online mode in action. Data is streamed from an Emotiv EPOC headset. by Gursimran Singh (575288)

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Illustration of PyNoetic’s 2D and 3D simulation module with visual feedback. by Gursimran Singh (575288)

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PyNoetic’s unique pick-place configurable flowchart that offers a no-code option for non-programmers. by Gursimran Singh (575288)

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PyNoetic’s feature extraction module that supports time domain features, frequency domain features, time-frequency domain features, spatial features as well as Brain Connectivity m... by Gursimran Singh (575288)

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Pseudo live-stream of EEG data is generated, and a simple pick-and-place flowchart is designed for channel selection and filtering. by Gursimran Singh (575288)

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List of Abbreviations by Gursimran Singh (575288)

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PyNoetic’s stimuli generation and recording module, which supports both ERP and SSVEP. by Gursimran Singh (575288)

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PyNoetic’s channel selection module, which supports various channel selection criteria including CSP, correlation, Mutual Information, and Chi-squared. by Gursimran Singh (575288)

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Tunable parameters in stimuli generation module. ^<i>a</i>The fixation cross is displayed once before an experiment starts to help subjects concentrate. <sup><i>b</i></su... by Gursimran Singh (575288)

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The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

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PyNoetic’s pre-processing module, which supports filtering and artifact removal, including ICA. by Gursimran Singh (575288)

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Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task. by Gursimran Singh (575288)

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Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. by Gursimran Singh (575288)

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A typical BCI system depicting the flow and processing of EEG data. by Gursimran Singh (575288)

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Algorithm used in the model to automatically estimate the parameters of LD pharmacodynamics, via minimization of a cost function. by Mauro Ursino (144905)

The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms. by Hamed Khodadadi (21446172)

“…<p>The final values of the cost function, the selected features number, and the implementation time for the introduced meta-heuristic algorithms.…”

Power flow and cost optimization with APSO variant-16 of APSO algorithm (b). by Muhammad Ahmad Iqbal (11851338)

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Power flow and cost optimization with APSO variant-16 of APSO algorithm (a). by Muhammad Ahmad Iqbal (11851338)

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Parameters and variables used to generalized cost function. by Zhipeng Huang (1759759)

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Efficient Parameterization of Density Functional Tight-Binding for 5<i>f</i>‑Elements: A Th–O Case Study by Chang Liu (35901)

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