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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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Pacemaker Artifacts

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Dr. Rishabh Pathak

Pacemaker artifacts are a type of electrical cardiac artifact that can be observed in EEG recordings. 

1.     Pacemaker Artifacts:

o Description: Pacemaker artifacts result from the electrical signals generated by cardiac pacemakers and can be picked up by EEG electrodes.

o    Characteristics:

§  High-Frequency Polyphasic Potentials: Pacemaker artifacts typically exhibit high-frequency polyphasic potentials with a shorter duration compared to ECG artifacts.

§  Distribution: These artifacts may have a broader field of distribution across the head compared to other types of cardiac artifacts.

o    Identification:

§ Appearance: Pacemaker artifacts can appear as very brief transients with higher amplitudes in channels including specific electrodes (e.g., A1 and A2), and may be evident diffusely in some occurrences.

§ Synchronization: Simultaneous occurrences of pacemaker artifacts with similarly appearing discharges in the ECG channel can indicate a permanent pacemaker source.

Understanding the characteristics and distinctive features of pacemaker artifacts in EEG recordings is essential for accurate interpretation and differentiation from other types of artifacts or genuine brain activity. Proper identification and differentiation of pacemaker artifacts can help ensure the quality and reliability of EEG data for clinical analysis and diagnosis.

Pulse Artifacts

Pulse artifacts are a type of mechanical cardiac artifact that can be observed in EEG recordings. 

1.     Pulse Artifacts:

o Description: Pulse artifacts result from the mechanical effects of the circulatory pulse on EEG electrodes, leading to waveform distortions in the recorded signals.

o    Characteristics:

§  Source: Associated with the pulsatile force of the circulatory pulse on the electrodes resting over scalp blood vessels.

§  Appearance: Pulse artifacts manifest as slow waves following the ECG peak, often exhibiting periodicity and a regular interval related to the cardiac cycle.

o    Identification:

§  Location: Pulse artifacts commonly occur over frontal and temporal regions but can be present anywhere on the scalp.

§  Alteration: Applying pressure to the electrode producing the artifact can alter its appearance on the EEG recording, aiding in identification.

o    Differentiation:

§ From ECG Artifacts: Pulse artifacts can be distinguished from ECG artifacts by their waveform characteristics and source related to the circulatory pulse.

§ From Other Artifacts: Understanding the unique waveform and periodicity of pulse artifacts helps differentiate them from other types of artifacts in EEG recordings.

Proper identification and differentiation of pulse artifacts in EEG recordings are crucial for accurate interpretation and analysis. Recognizing the distinctive features of pulse artifacts can help researchers and clinicians distinguish them from genuine brain activity and other types of artifacts, ensuring the quality and reliability of EEG data for clinical assessments and research purposes.

 

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