Welcome to my talk on the exploration of person-independent BCIs for internal and external attention detection and augmented reality.
My name is Lisa-Marie Vortman and I'm from the Cognitive Systems Lab of the University of Bremen, Germany.
One of the obvious advantages of Augmented Reality is that it combines real and virtual content.
This can be used to display helpful information, for example, for industry workers.
One of the downsides is that it highly increases the cognitive load because it adds possible sources of distraction.
That means that while we use it, we need a more focused attention.
Attention is this concept that we filter task-related sensory input and thoughts from task-unrelated information.
Our attentional mechanisms are manifold. While we can shift our attention, for example, to one of our senses: visual, auditory, tactile,
it can be also a part of visual attention that is in our focus, in our visual focus or in our periphery.
Or we can split our attention between multiple targets or focus on just one.
Attention can also be directed either internally or externally.
Examples for internally directed attention would be thoughts, memories or mental arithmetic.
And for externally directed attention
we could think of a visual search task, a multi-speaker listening scenario, or when you're waiting for a touch.
This sensory salient information might capture our attention in times where we don't necessarily want that.
So, for example, if we're internally focused and have a task-related,
internally directed attention, then we don't want to get distracted by salient external input.
And most of the virtual information that is displayed in Augmented Reality is very salient.
The motivation for the study is that in general, we want to add attention-awareness to an Augmented Reality setting by using a BCI.
This could lead to new applications and our main idea is to increase the usability of head-mounted devices.
We already did a study before where we showed that the usability increases when we add attention-awareness in an SSVEP-based smart-home environment.
One of the problems that we encounter besides the complicated setup for a BCI is that we
have relatively long training periods for EEG-based BCIs because they're usually person-dependent.
Our solution, and this is what this study aims at,
is to find a person-independent setup that is possibly training-free and, thus, increases the usability of such BCIs.
For the study, the experimental setup was described before in another publication, so please refer to that if you're interested in that in detail.
The collected data was of 15 healthy participants,
three of which had previous experience in AR. One dataset had to be excluded due to technical problems.
The data consisted of 15 to 20 second windows of either internally or externally directed attention.
This data was labeled. We recorded 16 channels of EEG data and binocular eye tracking and additionally, we had the participants fill out some questionnaires.
Our main task was to classify these data windows and label them as either internally
or externally directed attention and then see how accurate these labels can get.
I'm going to present to you the results for time reasons. If you're interested in the exact methods, please refer to the paper.
First, we performed a modality comparison. What we found is that for the data,
we were able to classify the EEG with an average accuracy of a little above 80 percent. The eye tracking data
was classified with an average accuracy of only sixty-seven percent.
If we combine the two feature sets we did reach a better accuracy of a little above eighty two percent.
However, this improvement was not significant compared to the EEG data.
We also looked at different classifiers.
We compared a vanilla neural network, a random forest, a linear discriminant analysis, and a convolutional neural network,
and we tested all of them on different window lengths. So we tried one-second two-second, four-second, seven-second and thirteen-second windows.
What we found is that in general, the LDA and the CNN outperformed the random forest and the vanilla neural network.
And that the longer the windows were, the higher our accuracies were.
In the next step, we pooled the data set of all participants and tried to figure out what happens when we don't train person-dependently.
And what we found was that the neural network, specifically the convolutional neural network, outperformed the LDA for this data set.
What you can see here is the 95-percent confidence interval for a 10-fold cross-validation.
And you can see that the four-second and seven-second windows for the neural network reached the highest accuracies.
If we do a true person independent classification in a live one out approach, the accuracy is vary greatly between participants.
On average, we were a little below 60 percent and our 95-percent
confidence interval is fifty-four to sixty-five percent.
We also trained on a selected training set.
That means seven participants with the highest classification scores from the first approach were chosen for the model training.
This increased the accuracy slightly. If you have any questions, feel free to contact me.
These are references. Thank you.