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Unveiling Hidden Neural Codes: SIMPL – A Scalable and Fast Approach for Optimizing Latent Variables and Tuning Curves in Neural Population Data

Dr. Rishabh Pathak
This research paper presents SIMPL (Scalable Iterative Maximization of Population-coded Latents), a novel, computationally efficient algorithm designed to refine the estimation of latent variables and tuning curves from neural population activity. Latent variables in neural data represent essential low-dimensional quantities encoding behavioral or cognitive states, which neuroscientists seek to identify to understand brain computations better. Background and Motivation Traditional approaches commonly assume the observed behavioral variable as the latent neural code. However, this assumption can lead to inaccuracies because neural activity sometimes encodes internal cognitive states differing subtly from observable behavior (e.g., anticipation, mental simulation). Existing latent variable models face challenges such as high computational cost, poor scalability to large datasets, limited expressiveness of tuning models, or difficulties interpreting complex neural network-based functio...
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Surface Electromyography Artifacts

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Dr. Rishabh Pathak

Surface electromyography (EMG) artifacts in EEG recordings are a common type of artifact caused by electrical activity in muscles near the recording electrodes.

1.     Description:

o    Surface EMG artifacts result from the electrical activity of muscles near the EEG electrodes, contaminating the EEG signal with muscle-generated electrical signals.

2.   Characteristics:

o High Amplitude: EMG artifacts often have higher amplitudes compared to brain-generated signals, making them easily distinguishable.

o    Frequency: Surface EMG artifacts typically exhibit higher frequencies, especially during muscle contractions.

o    Localization: These artifacts commonly occur in regions with underlying muscles, such as the frontalis and masseter muscles.

3.   Identification:

o    EMG artifacts can be identified by their distinct waveform characteristics, higher amplitudes, and frequency ranges that differ from typical EEG patterns.

o    The presence of sharp contours and less rhythmicity in the waveform can help differentiate EMG artifacts from brain-generated activity.

4.   Distinguishing Features:

o    EMG artifacts may co-localize with regions of maximum beta activity, resembling beta activity but with waveform differences.

o    The waveform of EMG artifacts is sharper and less rhythmic, especially when the high-frequency filter is set above 50 Hz.

o    EMG artifacts within the beta frequency range may appear as individual EMG potentials with durations of less than 20 milliseconds, separated by intervals that give them a beta frequency range appearance.

5.    Clinical Impact:

o    Proper identification and mitigation of surface EMG artifacts are crucial for accurate EEG interpretation and diagnosis.

o    Failure to recognize and address EMG artifacts can lead to misinterpretation of EEG findings and incorrect clinical decisions.

Understanding the characteristics and impact of surface EMG artifacts is essential for EEG technologists and clinicians to ensure the quality and reliability of EEG recordings for accurate clinical assessments and patient care.

 

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