The study explored how Mobile Brain-Body Imaging can be used to study the brain during real-world actions – like basketball free throws. It looks at brain signals linked to movement preparation. It also highlights how MoBI is changing the way we study human behavior outside the lab.
How researchers took this study to the basketball court
To test their idea in a realistic setting, the researchers recruited 26 basketball players. All participants were with at least a few years of experience and playing regularly. Authors described them as not complete beginners, but they also weren’t elite professionals.
What makes this study especially interesting is the setup. The researchers built a surprisingly simple system: smartphone, mobile EEG headset, and a small motion sensor worn on the wrist.
To record EEG, they used mbt’s Smarting PRO EEG system. And for motion tracking the participants wore Xsens’ wearable sensor. Check out the integration page for specific details on how to integrate our systems!
While recording brain activity and video, they also tracked body movement in real time using AI-based pose detection. Everything was synchronized using LSL so brain signals and body movements could be analyzed together. The whole setup could be used directly on a basketball court!
Mobile brain-body imaging setup with EEG (mbraintrain) and body motion tracking (Xsens) during free throws. Two smartphones stand on tripods and communicate via LSL. For motion tracking, scientists used a detection algorithm that relies on convolutional neural networks.The procedure itself was straightforward but carefully controlled. Each participant took 120 free throws. They were spread across several short blocks with breaks in between to avoid fatigue.
Players were told to take their time, prepare naturally, and shoot whenever they felt ready. To keep things consistent, they weren’t allowed to dribble between shots, and each ball was handed to them by an experimenter. During the game, the goal was to capture both how they prepare for the shot and how they execute it.
Challenges with mobile brain-body imaging
Many challenges arise in mobile brain-body imaging research. These include analyzing complex movements like free throws and dealing with motion artifacts in EEG data.
To find the start of movement, the researchers used pose detection to find a reference point. This is the moment the wrist reached eye level (“set-point”). Then, they worked backwards using wrist sensor data to identify the true onset of movement.
They defined this onset as the point where acceleration exceeded a baseline level. This created a reliable marker to align and examine the readiness potential (RP).
In mobile brain-body imaging setups, careful data cleaning is essential. The researchers focused on the time window around the shot. They filtered the data, removed noisy channels, and applied independent component analysis (ICA). ICA is used to separate brain components from artifacts.
We encourage you to check out the original study for more details on how they overcame some other challenges!
Validating movement detection
Acceleration magnitude over time across body parts (noted on the left of the graph). The dashed, red line represents the movement onset. The black dotted line marks the “set-point” as a time reference. Significant body parts at movement onset are marked with starts (p < 0.05).Before analyzing RPs, the researchers needed to confirm that the detection of movement onset worked. Using the wrist sensor together with pose data, they showed that several body parts – especially the hands and hips – were already moving at the detected onset time. This suggests their approach was reliable for capturing when a free throw begins.
Detecting readiness potential in preparation for body movement
(A) Body postures at key time intervals relative to movement onset. (B) Grand average RP topographies. They show increased negativity over fronto-central electrode sites leading up to movement onset (red line). (C) Readiness potential time-series over all channels – Cz electrode in blue line, other channels are in grey. In the same time frame, we can see wrist acceleration in a black line. (D) Topographical maps display significant regions (p < 0.05, red dots).The study clearly detected the RP before each shot. This brain signal appeared up to about a second before movement and was strongest in fronto-central areas of the brain (B, D). Which matches with previous findings. Even with full-body movement, the RP could still be observed using mobile brain-body imaging techniques.
Can readiness potential amplitude predict performance?
When comparing hits and missed shots, the researchers didn’t find any meaningful differences in RP amplitude. In other words, the level of brain preparation couldn’t predict whether a shot would go in or not. This was true both at the group level and when looking at individual players.
These results suggest that the RP alone is not a good indicator of performance in this context.
The grand average amplitude of the ERP recorded from the Cz channel, comparing successful (hits) and unsuccessful (misses) basketball free-throw shots, across all participants.How about the human pose?
In contrast, body movement told a more interesting story. Some players showed clear differences in their posture between hits and misses. Successful attempts were often linked to more stable positioning and better alignment of the arms and wrists. But these patterns weren’t the same for every player. It shows that performance does depend on individual movement styles rather than one “perfect” technique.
What this means for the future of MoBI?
This study shows how Mobile Brain-Body Imaging can bring neuroscience out of the lab and into real-world settings. Like a basketball court! The authors managed to obtain good EEG and motion capture data.
While readiness potentials don’t seem to predict performance on their own, combining them with movement data opens a much richer picture. As MoBI technology continues to improve, it could help us better understand sports performance, everyday behavior and even clinical conditions.
Check out the original paper linked here!
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