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Energy Manifold Natural Gradient Descent: From Riemannian Optimization to Modern Neuroscience, NeuroAI and Quantum Physics

Dr. Rishabh Pathak
When Geometry, Energy, Artificial Intelligence and Neuroscience Converge Modern Artificial Intelligence is rapidly moving beyond the idea that learning simply means minimizing an error function. Increasingly, researchers are asking a deeper question: what is the structure of the space in which learning takes place? This question becomes particularly important when the system being modelled is constrained, nonlinear, dynamic, or governed by physical principles. A recent work titled “Energy Manifold Natural Gradient Descent: Riemannian Optimization for Neural PDE Solvers” , by Zhangyong Liang and Huanhuan Gao, introduces Energy Manifold Natural Gradient Descent (EMNGD) , a mathematical framework that extends energy-based natural-gradient optimization from unconstrained Euclidean parameter spaces to constrained Riemannian parameter manifolds . At its core, the framework proposes a simple but powerful principle: An optimization algorithm should not only determine how to reduce error; it sh...
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Types of EEG Artifacts

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Dr. Rishabh Pathak

1.     Muscle Artifacts:

o Description: Caused by electromyographic (EMG) activity from muscle contractions.

o Characteristics: Higher amplitude, confluence of activity, and frequency overlap with EEG signals.

o Appearance: Diphasic and triphasic potentials with varying durations and locations.

o Distribution: Commonly observed in regions with underlying muscles, such as the frontalis and masseter muscles.

2.   Cardiac Artifacts:

o Description: Result from electrical and mechanical effects of cardiac activity.

o Characteristics: Time-locked to cardiac contractions, co-occurring with ECG complexes.

o  Types:

§Pacemaker Artifact: Broad field, high-frequency polyphasic potentials with shorter duration than ECG artifact.

§Pulse Artifact: Periodic slow wave following ECG artifact's peak, commonly over frontal and temporal regions.

§ Ballistocardiographic Artifact: Waveform similar to pulse artifact but more widespread, associated with body movements during cardiac contractions.

3.   Environmental Artifacts:

oDescription: Result from devices in the patient's surroundings during EEG recording.

o Causes: Electrical fields surrounding devices or mechanical effects on the patient or the patient's bed.

o Common Artifact: Alternating current (AC) noise at 60 Hz in some regions and 50 Hz in others, affecting some or all EEG channels.

4.   Technical Artifacts:

oElectrode Artifacts: Arise from issues with electrode placement, impedance mismatches, or movement artifacts during recording.

o Amplifier Artifacts: Result from problems with the EEG amplifier, such as saturation, noise, or incorrect settings.

5.    Artifact Mimics:

oBenign Epileptiform Transients of Sleep (BETS): Resemble ECG artifacts but can be distinguished based on waveform characteristics and temporal correspondence to ECG signals.

o Focal Ictal and Interictal Epileptiform Discharges: Differentiated from ECG artifacts based on waveform features, location, and occurrence patterns.

Understanding the types of EEG artifacts and their distinguishing features is crucial for accurate EEG interpretation and diagnosis, as it helps in differentiating genuine brain activity from unwanted signals that can affect the quality of EEG recordings.

 

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