CVSA is the process of attending to a target without

overtly looking at it. The direction of CVSA can be

classified through noninvasive

Electroencephalography (EEG) signals. Our literature

review revealed that CVSA classification, although

thoroughly studied, has rarely been used in

addressing the needs of individuals with minimal to

no motor function (such as individuals with Locked-in

Syndrome). We collected EEG data from 4 healthy

participants (all female, ages 21-27) performing a

CVSA task by attending to the left or right. We then

preprocessed the data and applied machine learning

algorithms to classify the direction of CVSA. Due to

human error, we discarded one participant’s recorded

data. We discovered that the classification accuracy

ranged between 70%—74.07% for the remaining 3

participants. Our classification of CVSA direction met

the accuracy minimum threshold set by pioneers in

the Brain-Computer Interface (BCI) field. Therefore,

in this pilot study, we conclude that CVSA direction

can be classified through non-invasive EEG signals

with an acceptable accuracy and has the potential to

assist individuals with minimal to no motor function

with binary communication and control tasks.

Individuals in advanced stages of Amyotrophic Lateral

Sclerosis (ALS) entirely lose their motor function, and

do not have an effective form of communication or

control. Research shows that in these stages of ALS,

individuals are still aware of their surroundings [1]. The

challenge of lacking communication and interacting

with their environment affect people’s daily lives and

sense of independence. Therefore, our aim in this

study is to see if CVSA direction classification can be

mapped to binary communication or control output to

address the needs of these individuals.

Due to human error during recording, it was necessary to

discard one of the participant’s data. As a result, we had 3 sets

of data left to analyze. The remaining data showed that the

classification accuracy reached at least 70%. This

demonstrates that classification of CVSA direction is feasible

with acceptable accuracies, and it can potentially be used as an

EEG-BCI system to address the communication and control

needs of the individual with ALS.

In this pilot study, we can conclude that an EEG-BCI system to

classify the direction of CVSA has the potential to be used as a

device for communication and control for patients with ALS. In

our future work, we aim to improve our classification accuracies,

have a larger sample size, and eventually have participants who

have ALS participate.

Covert Visuospatial Attention (CVSA) Direction Classification to assist individuals

lacking motor function with communication and control

Brooke C. Follansbee, Maysam M. Ardehali, Qussai M. Obiedat, Olawunmi George, &

Roger O. Smith

Figure 1. EEG Cap

Figure 2. Biosignal Amplifier

1. N. Birbaumer and A. Rana, “Brain-computer interfaces for communication in paralysis,” in Casting Light on the

Dark Side of Brain Imaging, A. Raz and R. T. Thibault, Eds. Academic Press, 2019, pp. 25–29.

2.Danny Plass-Oude Bos, Matthieu Duvinage, Oytun Oktay, et al. Looking around with your brain in a virtual world.

2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB): IEEE; 2011.

Acknowledgments

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Figure 3.

ABSTRACT

BACKGROUND

CONCLUSION

RESULTS

Participants in this study wore a non-invasive EEG cap. This cap tightly

fits around the participant’s head and holds the 16 electrodes in

place. The electrodes were positioned mainly over the parietal and

parieto-occipital regions of the brain [2]. A gel is then placed under

the electrodes so that the device is able to pick up brain signals. EEG

is the electrical activity of the brain cortex, and it can be observed

through non-invasive means by measuring the electrical activity over

the scalp. An EEG-BCI system collects brain signals and through

processing the signals, generates a variety of outputs.

METHODOLOGY