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Robotics in Neurorehabilitation: Beyond the Hype—Understanding What It Can (and Cannot) Do

Dr. Rishabh Pathak
Over the past decade, robotic neurorehabilitation has become one of the most discussed innovations in neurological recovery. Robotic gait trainers, upper-limb rehabilitation systems, exoskeletons, and AI-assisted rehabilitation devices are increasingly being adopted by hospitals and rehabilitation centres worldwide. However, an important question remains: Are robots the future of neurorehabilitation—or are they simply another tool in the rehabilitation toolbox? As clinicians and researchers, we must move beyond marketing claims and focus on scientific evidence, patient selection, and clinical reasoning. What is Robotic Neurorehabilitation? Robotic neurorehabilitation involves the use of electromechanical devices that assist, guide, resist, or augment movement during therapy. These technologies include: • Robotic gait trainers • Wearable exoskeletons • Upper limb robotic rehabilitation devices • End-effector robotic systems • Sensor-based rehabilitation platforms • AI-assiste...
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Mechanic Noise from devices Artifacts

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Dr. Rishabh Pathak

Mechanical noise from devices artifacts in EEG recordings can result from various mechanical devices present in the environment. 

1.     Description:

oSource: Mechanical devices such as ventilators and circulatory pumps can produce artifacts with slower components compared to electrical noise artifacts.

oCharacteristics: The artifact from mechanical devices may resemble ballistocardiographic or other electrode artifacts, but the waveform is typically distinct from random electrode artifacts.

2.   Characteristics:

oWaveform: Mechanical artifacts often exhibit rhythmicity related to the vibrations produced by the device, appearing as slow waves or complex patterns with a mixture of frequencies.

oRecurrence Pattern: Similar to electrical noise artifacts, mechanical artifacts characteristically recur with a fixed pattern according to the device's settings.

3.   Identification:

oDifferentiation: The distinct waveform and rhythmicity of mechanical noise artifacts can help differentiate them from other types of artifacts or physiological signals in EEG recordings.

oAssociation: Observing for a temporal association between the device's mechanical activity and the appearance of rhythmic artifacts can aid in confirming the presence of mechanical noise artifacts.

Understanding the characteristics and patterns of mechanical noise from devices artifacts is crucial for distinguishing them from brain-generated activity or other types of artifacts in EEG recordings. Proper recognition and differentiation of these artifacts contribute to the accurate interpretation of EEG data and help ensure the quality and reliability of EEG analysis in clinical practice and research.

 

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