If you’re a frequent reader of our Use-Cases page, you’ll notice we pride ourselves in the out-of-lab setups that are made possible using our mobile EEG systems.
However, sometimes even we are surprised by what it can do – like in this pilot study on EEG and swimming (in a real pool… filled with water)
The authors had a goal to extend CMI (cognitive-motor interference) research into new territory – by investigating the feasibility of using mobile EEG during swimming. This is the first known study to record brain activity in humans during free movement in water. There are prior studies in aquatic environments, but they involved animals or stationary human setups.
The study focuses on the effects of swimming on ERP components like P300 and N100, as well as changes in brain oscillations.
How exactly is an EEG measurement possible under water?
With some imagination and craftiness!
Simplicity of the Smarting PRO system helped, but there were more things to think about. The amplifier had to be protected as it’s not waterproof, so the electrodes and device were placed under a swimming cap.
The cap edges were then taped to the participant’s head so the water doesn’t go through. This was also important to keep the electrodes from bridging.
During swimming, participants had to perform an auditory task (more on this in the next chapter). The EEG amp can stream data via Bluetooth to a mobile phone, so researchers used it to present auditory stimuli too.
On the mobile phone EEG + stimuli triggers were synchronized using a wireless communication protocol – LSL.
Finally, the phone was floating on a buoy next to the swimmers the entire time.
Recording setup in the pool during swimming.Study included swimming as a primary task + auditory oddball task as secondary
Dual-task paradigm is used to investigate CMI, since it simulates real-world situations where individuals manage both cognitive and motor tasks simultaneously. In the end, it helps understand how the brain allocates resources when faced with these concurrent demands.
In this study, the primary task is swimming (motor), and the secondary is the oddball task (cognitive). The oddball task requires the swimmer to count target tones and ignore the other one – standard. In the end of the block, they report the number of target sounds as “proof” they actually focused on the task.
Researchers defined 3 different conditions in which participants did the oddball task. Pre- and post-swimming where they were sitting, and during swimming.
Data cleaning was very important due to movement during swimming
To prevent excessive artifacts, authors made sure to restrict head movement during swimming. They did this by giving the participant a snorkel, so they didn’t have to turn their heads to breath in.
To remove other artifacts that appeared during swimming, they used the ASR algorithm + ICA decomposition of data to find non-brain sources (such as muscle noise).
The amplifier has integrated IMUs which were also used to track swimmer’s movements. To be specific, scientists used accelerometer data to pin-point the time in which participants did a turn in the pool. Let’s see what they found!
Using the appearance of N100 component to verify the setup
Due to the novelty and complexity of the setup, authors wanted to confirm the experiment was carried out successfully. To do that, they looked at the N100 component which is a sensory response of the brain. So, they can use it to verify the swimmers did indeed hear the sounds during all conditions.
Let’s take a look at the grand average ERP waveforms:
Grand average ERPs on different central electrode sites in three conditions (a) pre swim (b) during swim (c) post swim. In red is the target sound response, while in blue is the standard sound.We can clearly see the presence of N100 component, which was also visible on single-subject analysis.
P300 effects however were not consistently observed
While prior studies demonstrated larger P300 amplitudes for target tones and CMI effects in dual-task, here the results were mixed.
P300 effects were not consistently observed, potentially due to small sample size (n = 9). It could also have to do with participant fatigue due to swimming.
The dual-task complexity might have also contributed to participants prioritizing swimming over the cognitive task, in line with a “not drowning first” strategy. Yet, even in the sitting condition, not all participants showed the expected effects.
Average P300 amplitudes after standard (Sta) and target (Tar) stimuli during the experimental blocks. The lines connect the dots belonging to single subjects. We can see a common trend here, however, only for one participant this was statistically significant.Oscillatory brain activity during turns in the pool, which require more mental effort
To look into the frequency domain of the recorded EEG, scientists chose turns during laps in the pool. This activity requires more coordination since it’s not repetitive as swimming in a straight line. They found the moments of turns in EEG recordings by analyzing motion data.
An example of the Accelerometer Z-axis waveform for one swimmer. We can see the peaks that correspond to turns in the pool.About oscillatory brain activity, the study observed significant changes in alpha/mu (8-13Hz) and beta (15-30Hz) during turns. These are generally associated with movement preparation and coordination. This suggests movement-related brain activity, though more research is needed on this matter.
(A) frequency power during arbitrary 5s in sitting condition while resting. (B) frequency power 2.5s before and after a turn at 0s on the spectrogram. (C) mean frequency power in A and B. (D) Topographies during different phases before and after turn, for both alpha/mu and beta bands. (E) Frequency power during turns, normalized by the mean sitting baseline. Significantly lower or higher dB change from the baseline is marked with a star.The study demonstrated the feasibility of recording meaningful EEG during swimming. Despite some limitations, the findings highlight the potential for expanding mobile EEG applications to more complex, real-world settings. Thus, paving the way for future advancements in CMI and other research.
Read the original paper here.
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