We present Brainwear: towards multi-modal garment integrated EEG.
Brain activity has acquired an increasing interest in the wearable field.
As we can appreciate most current commercial wearable EEG devices have low consumer acceptance
because they are neither aesthetically pleasant nor socially discrete.
Additionally, studies with these devices are constrained to laboratory environments with
restricted movements.
The concept of "Transparent EEG" was defined by Bleichner and used by other studies to
focus on a combination of features of mobile sensing applications, including self-applicable,
motion-tolerant, near-invisible systems that are suitable for long recordings.
In the wearable Human Activity Recognition, EEG has already shown great potential.
It has been used in several studies to provide information on many psychological and cognitive
aspects.
Despite that, none of these studies has seamlessly integrated EEG with other physiological modalities
in a single embedded system.
The wearable EEG enabler is the latest generations of chips integrating EEG Analog Front Ends,
such as the ADS1299 that provides 8-channels in a compact package, replacing cumbersome
circuit assembly.
Open-source toolkits such as OpenBCI provide hardware and software suites using EEG in
a complete embedded system.
However, if other wearable researchers want to integrate more wearable components, they
will need to engineer the components into the EEG system, which has many limitations
because it is designed to only host the EEG function, such as an out-dated micro-processor.
Using the opposite approach, we propose Brainwear: an open-source, transparent EEG sensing module
that is pin-compatible with the Feather family and based in two major design considerations:
first, simplicity by packaging all the EEG components in a plug and play module
And compatibility with the open and popular standards, so that it will not be locked-in
by legacy technology.
With our approach, the researchers or designers can choose their own controller and wireless
platform, and integrate the EEG function just as if any other embedded modules.
We validated our approach with a soft EEG cap prototype and a study based on the common
approach in neuroscience: the single n-back task with visual stimuli.
The idea is to classify five different levels of memory workload: resting state with eyes
closed, eyes opened, and three states with increasing memory load: 1-, 2-, and 3-back.
The figure shows the experiment time course in which only the windows in the Capturing
Stage are considered for classification of n-back states as the participants are properly
engaged in the task.
We employed the Shallow ConvNet, a CNN-based deep neural network model, commonly used in
EEG and performed Leave-one-session-out cross-validation.
The results show that the resting state and cognitive load states are well separated achieving
similar classification as other neuroscience studies with medical instruments.
We finally demonstrate how Brainwear can be integrated seamlessly with other sensing modalities
such as a Force Sensitive Resistor (FSR) and a piezoelectric within a single sensor probe.
The embedding of the entire setup is presented in a baseball cap
This is the recording of a person wearing the cap while performing different head movements
and facial expressions.
The experiment starts with head movements with eyes open in which we can see that the
FSR and piezoelectric reveal Ballistocardiogram activity on the forehead.
Furthermore, we can also appreciate that piezo and FSR complement the EEG signals by detecting
gross motion artifacts such as nodding, and shaking the head.
The cap also allows to observe a well-known neural process called the "Berger effect"
that exhibits an increase in the posterior alpha frequency power between 8 and 12 Hz
when closing the eyes.
Finally, our experiment shows how the wearable system could offer information on the wearer's
facial expression, whereas the EEG data would have been discarded as a motion artifact in
neuroscience.
To sum up, We developed an open-source, transparent EEG sensing module that is pin-compatible
with the Feather family.
We employed a garment prototype based on the standard neuroscience that achieves performance
on par with neuroscience state-of-the-art.
Further integration with mechanomyography shows how EEG and other sensing modalities
can be integrated into one wearable system to provide complementary information about
user’s neural and physiological activities.
In this video we can appreciate the multi-modal signals from the soft cap while a person performs
different gestures and head movements.
We believe our approach will enable more out-of-the-lab research opportunities that combine neural
and physical activities, such as cognitive processes during sports activities.
Thank you for your attention.