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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Linear-Scaling Local Natural Orbital CCSD(T) Approach for Open-Shell Systems: Algorithms, Benchmarks, and Large-Scale Applications by P. Bernát Szabó (10526316)

“…The accuracy of open-shell LNO-CCSD­(T) is extensively tested for radicals and reactions thereof, ionization processes, as well as spin-state splittings, and transition-metal compounds. At the size range where the canonical CCSD­(T) reference is accessible (up to 20–30 atoms), the average open-shell LNO-CCSD­(T) correlation energies are found to be 99.9 to 99.95% accurate, which translates into average absolute deviations of a few tenths of kcal/mol in the investigated energy differences already with the default settings. …”

Long and fast fixation times on a four-column graph <i>G</i>_<i>N</i>. by David A. Brewster (17118328)

Data fitting and inference of the time-dependent parameters by TDINN algorithm for multiple waves of COVID-19 infection in Liaoning province. by Mengqi He (5753525)

Solving Fused Penalty Estimation Problems via Block Splitting Algorithms by Tso-Jung Yen (4247998)

Survival outcome missing at random (MAR) and predictors missing completely at random (MCAR): Imputation performance (sensitivity, specificity, percentage correctly classified [PCC]... by Alpha Forna (11436837)

Survival outcome missing not at random (MNAR) and predictors missing completely at random (MCAR): Imputation performance (sensitivity, specificity, percentage correctly classified... by Alpha Forna (11436837)

The Passive Voice in Artificial Intelligence Language: Algorithmic Neutrality and the Disappearance of Agency by Agustin V. Startari (21179732)

“…Abstract This article explores the use of passive voice in texts generated by artificial intelligence systems, examining how algorithmic language reproduces the illusion of neutrality by structurally erasing agency. …”

CPU temperatures during the benchmark test. by Dong-Jun Lee (14068673)

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Comparison of running times. by Dong-Jun Lee (14068673)

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Tools included in the NGSpop software. by Dong-Jun Lee (14068673)

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