High-Density EEG and EMG: The Body That Grasps, Feels and Organizes Movement

A BrainLatam2026 reading on EEG, EMG, BCI, motor control, APUS, Tensional Selves, rehabilitation and technical research architecture

Before thinking of the brain as a command center, we need to look at the hand.

The hand does not merely obey.
It explores, measures, adjusts, feels, predicts and decides together with the body.

When we grasp a cup, hold a pen, lift a box, play an instrument or manipulate a tool, there is not only “movement.” There is a continuous negotiation between intention, vision, muscle, force, posture, bodily memory and the world.

That is why the publication “High-Density EEG and Multi-Muscle EMG Dataset during Object Prehension with a sensorized Grasping Box in Humans”, by Lomele, Lencioni, D’Ambrosio and colleagues, published in Scientific Data in 2026, is especially valuable.

The article presents an open dataset combining high-density EEG and multi-muscle EMG during object prehension movements. The researchers recorded 64-channel EEG together with EMG from 13 upper-limb muscles in 14 healthy participants, during visually guided prehension tasks using a custom sensorized Grasping Box.

The central question can be stated as:

How do cortical activity and muscular activity organize together during different human forms of grasping an object?

This question is powerful because grasping seems simple, but it is one of the most sophisticated human actions. To pick up an object, the body must integrate vision, intention, posture, muscle synergies, finger control, force, timing and sensory feedback.

The strength of this article is that it does not reduce movement only to the brain. It also does not reduce movement only to the muscle. The authors created a dataset where EEG, EMG and behavioral events are synchronized in time, allowing researchers to study how cortical dynamics and muscle recruitment appear in each phase of action.

The experimental design is very elegant. Participants performed three grip types:

precision grip, with thumb and index finger;
whole-hand power grasp, with the full hand;
unconventional grip, with thumb and ring finger.

This third grip is especially interesting because it is less habitual and requires finer digit control. It allows researchers to compare canonical daily movements with a non-conventional motor configuration.

The Grasping Box was created to mark precise action events: LED-on, functioning as the go signal; touch event, when contact with the object occurs; lift event, when the object is lifted; and LED-off, marking the end of the holding phase.

This detail is fundamental. The body does not act as one continuous block. It moves through transitions: preparation, reaching, contact, grasping, lifting, holding and release. Each moment may involve different cortical rhythms, different muscle synergies and different Tensional Selves.

The researchers deserve explicit recognition for their methodological care. Synchronizing EEG, EMG, contact sensors, lift detection and task events requires engineering, temporal precision, signal processing and a very well-constructed scientific question. The study offers a valuable foundation for neuroscientists, engineers, clinicians and rehabilitation researchers interested in motor control, sensorimotor integration, muscle synergies and adaptive BCI — Brain–Computer Interface systems.

Equipment, amplifiers and sensors used in the study

This article is also important for researchers and laboratories because it describes the technical architecture used for data collection.

For EEG, the authors used a BrainAmp DC amplifier, from Brain Products, in a 64-channel configuration. Signals were recorded from 62 scalp electrodes, positioned according to the international 10/20 system, with a sampling rate of 1 kHz. Two additional electrodes were used for EOG, positioned at the outer canthi of the eyes, allowing eye movements and blinks to be monitored for later artifact correction.

For EMG, the authors used wireless superficial bipolar WavePlus sensors, from Cometa Systems Srl. The sensors were positioned on 13 muscles of the dominant upper limb, covering proximal muscles, forearm muscles and intrinsic hand muscles. This makes it possible to observe arm stabilization, wrist control, finger flexion and extension, and fine digit control.

The Grasping Box was also instrumented with sensors and synchronization systems. The device included Arcol Ohmite FSR07BE pressure sensors to detect first contact with the target, a microswitch to detect the lift event, a Knightbright LED as the visual signal for task start and end, and an Arduino Uno REV3 unit to acquire and transmit behavioral events to the EEG and EMG acquisition systems.

This level of description is extremely valuable. It shows that a good experimental design does not depend only on the scientific question, but also on the compatibility between question, equipment, sensors, synchronization and signal analysis.

For laboratories working with EEG, EMG, NIRS/fNIRS, BCI and rehabilitation, this kind of technical detail helps answer a practical question:

What technical architecture is necessary to transform a question about movement into reliable data?

What the article really measured

The article measured synchronized electrophysiological and muscular activity.

The EEG allows researchers to observe cortical dynamics with high temporal resolution, making it suitable for studying motor preparation, movement-related cortical potentials, beta-band modulation, ERD/ERS and cortico-muscular interactions.

The EMG recorded activity from 13 upper-limb muscles, including proximal muscles for arm stabilization, forearm muscles for wrist and finger control, and intrinsic hand muscles for fine digit control.

The task structure allowed EEG and EMG signals to be aligned with the main motor events. This makes the dataset useful for studying:

motor planning;
grip selection;
muscle synergies;
sensorimotor integration;
cortico-muscular coherence;
dynamic and isometric motor control;
rehabilitation biomarkers;
BCI and assistive technologies.

From the BrainLatam2026 perspective, the most important point is this:

the dataset allows us to observe the moment when intention becomes gesture.

APUS, Tensional Selves and the hand as body-territory

This article speaks directly to APUS, understood as body-territory and extended proprioception.

When the hand grasps an object, the body does not simply touch something external. The object temporarily becomes part of the body’s field of action. The cup, the tool, the box or the instrument enters the sensitive territory that the body can stabilize, feel and transform.

This is APUS in movement: proprioception extending into the world.

The article also helps us think about Tensional Selves.

A precision grip creates one type of bodily self: fine, focused, delicate, controlled.
A whole-hand grasp creates another: broad, strong, distributed, stable.
An unusual thumb-ring grip creates another: less habitual, more demanding, more reorganizational.

The “self,” here, is not only a mental narrative. It is a temporary configuration of muscle, attention, posture, intention and sensory prediction.

In the Damasian Mind, this is essential. The brain does not command a passive body. The body continuously informs the brain. Muscle is not only motor output. It participates in the living circuit through which the organism updates action, perception and self-experience.

The hand thinks in movement.

From the article’s question to a BrainLatam2026 experimental design

The article asked:

How can a synchronized dataset of high-density EEG and multi-muscle EMG be provided during human object prehension?

To answer this, the authors measured:

cortical activity with EEG, muscular activity with EMG and motor events with a sensorized box capable of marking initiation, contact, lifting and holding.

With this, the study offers:

an open dataset to investigate motor control, muscle synergies, sensorimotor integration, cortico-muscular coherence, rehabilitation and BCI.

From this contribution, BrainLatam2026 can ask:

How do different Tensional Selves emerge when a person grasps objects with different levels of precision, force, familiarity, risk, affect or belonging?

This new question requires a compatible experimental design, combining:

high-density EEG + multi-muscle EMG + force sensors + kinematics + eye-tracking + respiration + HRV/RMSSD.

EEG is necessary because the question involves motor preparation, attention, error, action planning and fast cortical dynamics.
EMG is indispensable because the Tensional Self is expressed in muscles: hand, forearm, arm, shoulder, neck, jaw and posture.
Force sensors show how much pressure and stability the body applies.
Kinematics reveals trajectory, speed and precision.
Eye-tracking shows how gaze anticipates gesture.
Respiration and HRV/RMSSD help identify whether the body is in effort, regulation, alertness or fluidity.

If the question involves cooperation — for example, two people manipulating an object together — we could include Hyperscanning with EEG or fNIRS/NIRS to investigate brain-to-brain synchrony, leadership, coordination and motor Jiwasa.

If the question involves prosthetics, assistive robotics or post-stroke recovery, the field of BCI becomes even more relevant, because EEG and EMG signals can help decode motor intention and support adaptive interfaces.

Technology, therefore, emerges from the question.

We do not use EEG and EMG because they are sophisticated devices.
We use EEG and EMG because we want to understand how intention and muscle become coupled in a concrete action.

BCI, fNIRS, rehabilitation and academic research

This article is highly relevant for researchers working with BCI, neuroengineering, prosthetics, neuromotor rehabilitation, stroke, spinal cord injury, Parkinson’s disease, upper-limb control and motor biomarkers.

A good brain-computer interface cannot depend only on an abstract cortical reading. Real human movement emerges from the relationship between brain, muscle, object and environment. That is why datasets combining high-density EEG and multi-muscle EMG are so important: they allow researchers to train models, test hypotheses and create systems closer to real human gesture.

fNIRS/NIRS can also enter as a complementary technology in future studies. While EEG offers excellent temporal resolution to capture the electrical dynamics of movement, fNIRS can help measure hemodynamic responses in prefrontal and motor regions during effort, motor learning, fatigue, rehabilitation and embodied decision-making. fMRI can contribute to complementary spatial mapping studies, although it is less compatible with natural movement.

For Brain Support and BrainLatam, this point is strategic: selling EEG or NIRS for academic research is not just selling equipment. It is helping laboratories formulate good experimental questions.

A laboratory may ask:

How does EEG change before contact with the object?
How does EMG reveal muscle synergies in different grip types?
How do post-stroke patients reorganize Tensional Selves during prehension?
How can fNIRS complement EEG/EMG by measuring prefrontal effort during motor control?
How can BCI use EEG and EMG to improve prosthetics, assistive robotics or rehabilitation?

These questions show that good technology needs a good question. And a good question emerges when we understand the body as a living system, not as an obedient machine.

A generous decolonial critique

Like every study situated in a specific scientific context, this publication opens space to ask how these data could be expanded in Latin American contexts.

Object prehension is not the same in every territory. The hand of someone who plays piano, the hand of someone who works in agriculture, the hand of someone who sews, the hand of someone who cares, the hand of someone who builds, the hand of someone who cooks and the hand of someone relearning movement after a stroke all carry different bodily histories.

From Decolonial Neuroscience, the question is not only:

How does the human hand grasp an object in the laboratory?

But also:

How do different body-territories learn, repeat, suffer, create and reorganize themselves through the hands?

In technical schools, workshops, public hospitals, traditional communities, rehabilitation centers and music laboratories, EEG, EMG and fNIRS/NIRS can help study movements that make sense for each territory.

Bridge with DREX Cidadão

The connection with DREX Cidadão appears when we think about rehabilitation, work and autonomy.

A society that wants to reduce social Zone 3 must allow injured, aging, exhausted or excluded bodies to recover action, belonging and possibility. Technologies such as EEG, EMG, NIRS/fNIRS and BCI can help build more precise rehabilitation, better prosthetics, personalized motor protocols and public policies for functional care.

But this requires investment.
It requires a strong public health system.
It requires public research.
It requires equipped laboratories.
It requires access.

DREX Cidadão, as a minimum economic metabolism of the social body, can be understood as a policy that returns time, energy and presence so that people can receive care, relearn movement and return to collective life.

Closing

The publication by Lomele and colleagues reminds us that grasping an object is much more than closing the hand.

It is brain, muscle, vision, force, time, intention and world happening together.

From BrainLatam2026, this study shows that the body does not merely execute commands. The body thinks in movement. The hand organizes APUS. The muscle expresses the Tensional Self. And EEG with EMG helps us listen, with scientific rigor, to the instant when intention becomes gesture.

Perhaps one of the great tasks of Decolonial Neuroscience is this: to restore scientific, clinical and territorial dignity to human movement.

Because before writing theories, we grasp the world.

Reference

Lomele, G., Lencioni, T., D’Ambrosio, S., Comanducci, A., Lucchetti, F., Marzegan, A., Derchi, C., Garzonio, S., Atzori, T., Rabuffetti, M., Castiglioni, P., Ferrarin, M., & Fornia, L. (2026). High-Density EEG and Multi-Muscle EMG Dataset during Object Prehension with a sensorized Grasping Box in Humans. Scientific Data. https://doi.org/10.1038/s41597-026-07242-y