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Somatosensation

Somatosensation

The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.

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Related Experiment Video

Updated: May 10, 2026

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

Published on: August 2, 2016

Selective sensation based brain-computer interface via mechanical vibrotactile stimulation.

Lin Yao¹, Jianjun Meng, Dingguo Zhang

¹State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, China.

|June 14, 2013

Summary

This summary is machine-generated.

This study introduces a novel Brain-Computer Interface (BCI) using selective sensation, achieving high accuracy in distinguishing tactile stimuli. This method offers a promising alternative or complement to traditional motor imagery BCIs.

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Area of Science:

Neuroscience
Biomedical Engineering
Human-Computer Interaction

Background:

Brain-Computer Interfaces (BCIs) traditionally rely on motor imagery.
Existing BCI methods can be limited by user experience and cognitive load.
The somatosensory system's response to tactile stimuli offers a potential alternative control pathway.

Purpose of the Study:

To investigate the feasibility of using selective sensation perception as a BCI modality.
To compare the performance of a selective sensation BCI with motor imagery BCI.
To assess the potential of selective sensation to complement existing BCI systems.

Main Methods:

Mechanical vibrotactile stimulation applied to the wrists.
Selective sensation perception task designed for two-choice selections.
Analysis based on event-related desynchronization/synchronization (ERD/ERS) of brain activity.
Comparative experiments with motor imagery BCI.

Main Results:

Six out of nine subjects achieved over 80% discrimination accuracy.
Three subjects exceeded 95% accuracy in the selective sensation task.
Selective sensation BCI showed significant performance improvement compared to motor imagery BCI (e.g., >80% vs. ~65%).

Conclusions:

Selective sensation perception is a feasible and effective BCI modality.
This approach offers a complementary pathway to motor imagery for BCI control.
The proposed BCI has the potential to enhance the widespread adoption of BCI systems.