Overview of functionality supported by PyNoetic and its various modules, including the live analysis and programmable flowchart. by Gursimran Singh (575288)

Subjects:

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)

Subjects:

List of Abbreviations by Gursimran Singh (575288)

Subjects:

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. by Gursimran Singh (575288)

Subjects:

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

Subjects:

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

Subjects:

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)

Subjects:

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

Subjects:

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

Subjects:

Comparison of PyNoetic’s functionality for BCI design with other existing state-of-the-art frameworks. by Gursimran Singh (575288)

Subjects:

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

Subjects:

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

Subjects:

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

Subjects:

Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

Subjects:

Walltimes for the contact-frequency data for the MD datasets used in S2 Fig on a desktop computer, Intel Core i7-6700K CPU @ 4.00GHz, with 8 cores and 64GB RAM. by Guillermo Pérez-Hernández (21156182)

Subjects:

Correlation (a) and Mean-Absolute-Error (b) between frequencies at a reference cutoff, δ₀, and different frequencies, , at different cutoff-values, δ_scan ∊ [3... by Guillermo Pérez-Hernández (21156182)

Subjects:

Overview of mdciao’s API submodule and their functionalities. by Guillermo Pérez-Hernández (21156182)

Subjects:

Fig 4. - mdciao: Accessible Analysis and Visualization of Molecular Dynamics Simulation Data by Guillermo Pérez-Hernández (21156182)

Subjects:

Overview of the MD datasets used in S2 Fig. by Guillermo Pérez-Hernández (21156182)

Subjects:

Things to do after installing mdciao. by Guillermo Pérez-Hernández (21156182)

Subjects: