Classification of Absolutely Volitional Covert Visuospatial Attention (AV-CVSA) Direction Through EEG Signals: A Pilot Study


In advanced stages of amyotrophic lateral sclerosis (ALS), patients can reach a point where motor control can be virtually lost, leaving them with no means of communication and interaction with others and their environment (locked-in state). Our research project aims to test a novel, non-invasive electroencephalography-based (EEG) Brain-Computer Interface that is capable of accurately classifying the lateral direction of participants’ visual attention, as they voluntarily shift their Covert Visuospatial Attention (CVSA), with no head, neck, or eye movements or directional prompts. This project will identify the primary factors for optimizing the data collection paradigm and the EEG analysis methods, test a real-time implementation, and pilot it on a small group of participants with ALS as key steps toward developing an Absolutely Volitional-CVSA (AV-CVSA) control system as a method of basic communication and control suitable for immobile individuals.


ALS is an idiopathic progressive disease that dramatically reduces motor function, with much less impact on sensory functions. The annual incidence rate of ALS in the U.S. is about 5000 cases, with a prevalence of more than 16,000 individuals. More than 75% of individuals with ALS will ultimately require assistance for communication and control. Research has found that the loss of communication abilities is the main factor for more than 90% of individuals who decide to decline prolonging their lives on life-support. ALS patients in advanced stages are clinically classified into: 1) Locked-in State (LIS) in which patients maintain residual motor control, and 2) Complete LIS (CLIS), in which motor control is entirely lost.

To compensate for the lost motor abilities, ALS patients with mild to significant motor function loss commonly use Augmentative and Alternative Communication (AAC) devices for communication, and occasionally BCIs for control. AAC devices require some level of motor function, and thus are much less beneficial when the motor abilities of the patient decline severely. BCI systems can be either invasive (e.g. electrocorticography-ECoG) or non-invasive (e.g. electroencephalography-EEG) based. Invasive BCI systems require surgery and are not desirable due to potential risks and complications, such as neurologic and superficial infections, intracranial hemorrhage, elevated intracranial pressure, movement of electrodes, etc. Also, in advanced stages of ALS, the patient may not be able to provide consent for the surgery, hence raising ethical questions. However, most non-invasive strategies have substantially less spatial resolution than invasive methods, require training, and can be cumbersome to set up. Nevertheless, non-invasive EEG-BCIs allow for a very high temporal resolution, enabling the design of a real-time classification system.

BCIs allow individuals with minimal or no motor function to communicate and control devices by modulating their brain activity, eliminating the need for motor function. Currently, EEG-BCI systems are either stimulus-driven (bottom-up), or self-driven (i.e. voluntarily modifying neural activity, top down). Stimulus-driven EEG-BCIs usually rely on neural response to external stimuli (e.g. P300 paradigms or Steady State Visually Evoked Potentials), while self-driven EEG-BCIs rely on voluntary modulations of neural activity with minimal to no external stimulation (e.g. Motor Imagery paradigms). Stimulus-driven visual attention paradigms are difficult to use and rely heavily on visual cues and overt visual attention, i.e. looking directly at targets and stimuli. This presents a challenge for advanced ALS patients incapable of any motor function, including eye movements. Stimulus-driven paradigms have been tested with ALS patients (mainly in early stages) successfully. Self-driven paradigms require longer training, but if applied properly, are more intuitive to use and do not require any motor movement. An example of self-driven EEG-BCI is decoding the direction of Covert Visuospatial Attention (CVSA). CVSA paradigms do not require looking directly at a visual target (overt visuospatial attention). Instead, users are instructed to covertly attend to targets, without any eye, head, or neck movement, while fixating the focus of their gaze on a fixation point. CVSA paradigms show comparable classification accuracy against overt attention BCI paradigms, and they have been shown to generate almost the same pattern of brain activity over the visual cortex as overt attention to visual targets.

Currently, CVSA paradigms rely on visual displays and exogenous or endogenous cuing. With exogenous cuing, while the participant’s gaze is focused on the fixation point, a target appears directly at the location where the participant must covertly attend. With endogenous cuing, the cue appears at the fixation point and prompts the participant to covertly focus on one of the visual targets on the screen. CVSA paradigms have been tested, mainly on healthy participants, with promising results. Both these paradigms rely on external cues presented on a visual display.

To the best of our knowledge, decoding volitional shifts of CVSA in real-time through non-invasive EEG signals that rely on absolutely no external stimuli (endogenous or exogenous) has not yet been studied. Virtually nothing is known regarding how these volitional shifts affect the EEG signal, compared to other CVSA paradigms. An fMRI study by Gmeindl et al. helps our understanding of this function as they examined the role of volition in CVSA. While this MRI paradigm is not feasible for daily use by people with ALS in their living environments, this study identified loci of interest in the brain involved in volitional shifts of CVSA (many likely too small and/or too deep inside the brain to be studied with the low spatial resolution of EEG systems, e.g. medial superior parietal lobule, right middle frontal gyrus, basal ganglia, etc.). These identified loci are critical to understanding the underlying network of attending volitionally, and to our strategic monitoring of the high level more superficial neural activity of the occipital cortex. From a therapeutic and scientific knowledge perspective, learning about the effects of volition in modulations of neural activity over the parieto-occipital regions during CVSA shifts is essential in developing an EEG-BCI system that is sustainable and requires minimal equipment for operation and use by individuals incapable of performing any motor movements. In this project, we aim to develop an Absolutely Volitional – Covert Visuospatial Attention (AV-CVSA) paradigm, relying on no visual cues (endogenous or exogenous), as a novel approach to enabling BCI-EEG control.


We propose an Absolutely Volitional CVSA (AV-CVSA) paradigm to achieve a comparable accuracy in detection of CVSA direction, without providing any endogenous or exogenous cues. We choose non-invasive EEG over invasive (but more spatially accurate) methods such as electrocorticography (ECoG), to reduce the risks associated with invasive methods. Despite their low spatial resolution, non-invasive EEG-BCIs are more temporally accurate than other types of BCI, enabling the feasibility of implementing a real-time system for control and communication with high portability. Based on a successful demonstration of early pilot data, our central hypothesis is that since the visual cortex of the brain has a relatively large representation in the parietal and occipital lobes, we can detect CVSA direction with higher accuracies, compared to paradigms such as Motor Imagery. We seek to investigate and identify practical data collection and analysis methods that can lead us to reach high classification accuracy rates and implement our findings to develop a novel real-time AV-CVSA direction classification system for communication and control.

Objective 1) Develop and optimize an offline system to classify AV-CVSA direction in healthy participants and compare its performance with CVSA direction classification paradigms that use endogenous or exogenous cues. We hypothesize that AV-CVSA will perform more optimally than existing CVSA classification paradigms, and that providing CVSA training will result in a higher classification performance.

Objective 2) Develop and optimize a real-time classification system to determine the spatial direction of AV-CVSA in real-time, for healthy participants and individuals with ALS with significant motor impairments and compare the performance of the real-time classification system with offline classification. We hypothesize that the real-time AV-CVSA paradigm will accurately select dichotomous choices with the same performance as the offline method. We also hypothesize that the real-time AV-CVSA classification system can be optimized for effective use by ALS patients.

Achieving the objectives of this project will uncover previously unknown information about the role of volition in classification of CVSA through surface EEG. Our study will also provide a necessary and uncomplicated means of basic control and communication, in real-time, for patients who may decide to discontinue living on life-support solely due to the lack of communication and control capabilities caused by neurological diseases.


Preliminary studies: We collected EEG data from 4 healthy participants (ages 21-27), using our proposed AV-CVSA paradigm (providing no endogenous or exogenous cues for direction), to establish the feasibility of reliable AV-CVSA classification. We presented a fixation cross to our participants on a display in the center of their visual field. Participants told us before each trial a sequence of 3 directions to which they were going to attend (e.g. Left-Left-Right: LLR), and we recorded those directions in a chart. Each trial consisted of a sequence of 3 binaural auditory cues, with 3 seconds in between: Get Ready, Beep, and Bleep (Fig. 1). In each trial, the aforementioned sequence repeated 3 times. We instructed our participants to remember the directions to which they told us they will attend when they hear the Get Ready cue (e.g. L), then covertly attend to that direction while fixating their gaze on the fixation cross when they hear the Beep, and stop attending and focus on the fixation cross when they hear the Bleep.

Covert Visuospatial Attention data collection software screenshots
Figure 1: Covert Visuospatial Attention data collection software screenshotsView EqTD

Participants repeated the same process for the remaining directions (e.g. L and then R). We used neutral binaural auditory cues to avoid making the covert attention involuntary. Intertrial gap was controlled manually to avoid fatigue. To maintain consistency across different participants, we instructed them to covertly attend to the edges of the monitor for each direction. Participants were sitting in a neutral position, 66 inches away from the monitor, creating a 9.0-degree visual angle between the location of covert attention and the fixation cross. Overall, we recorded responses from each participant in 2 sessions: Session 1 (20 trials = 60 Ls & Rs), and Session 2 (40 trials = 120 Ls & Rs). We instructed our participants to avoid overtly looking at the target and to maintain the focus of their gaze on the fixation cross. We also instructed participants to inform the technician if they overtly looked at the target, blinked, or made any eye movements at all. We had no such incidents, and during the breaks and after data collection we confirmed protocol adherence with our participants.

EEG data were collected from 16-channels (g.USBamp; G.TEC Medical Engineering GmbH, Austria) according to the extended 10-20 system, and electrodes were mainly placed on the parietal and parieto-occipital regions (Fig. 2). For data recording and stimulus presentation we used BCI2000 (National Center for Adaptive Neurotechnologies: NCAN, Albany, NY). Then, the data were pre-processed using EEGLAB (Swartz Center for Computational Neuroscience, La Jolla, CA) by filtering (0.1 – 40 Hz), re-referencing (to average of all electrodes), epoching (1 second before to 3 seconds after the beep), removing baseline (average activity from 1 second before the beep to the onset of the beep), and marking each epoch with the corresponding direction of attention (epochs for left and right), based on our chart (Fig. 3). All epochs from Session 1 for all participants were marked. A total of 10 different machine learning (ML) methods were used to process the data: Gradient Boosted Decision Trees (XGBoost), Linear Discriminant Analysis (LDA), Quadrant Discriminant Analysis (QDA), Decision Trees (DT), Naïve Bayesian (NB), Deep Neural Networks (DNN), Linear Regression (LR), Random Forests (RF), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN). 80% of the epochs were randomly selected to train the classifier and the remaining 20% were used for testing, with 10-fold cross-validation.

Schematic of electrode locations to collect covert visuospatial attention activity from the brain
Figure 2: Schematic of electrode locations to collect covert visuospatial attention activity from the brainView EqTD

Flowchart of Covert Visuospatial Attention data preparation and pre-processing stages
Figure 3: Flowchart of Covert Visuospatial Attention data preparation and pre-processing stagesView EqTD

We used a moving time-window (MTW) and averaged the electrical activity in each window, to represent that portion of the data. The MTW started from after the onset of the cue, and averaged the activity inside the window, then moved forward one step until the 3 seconds after the cue were covered. The length of MTWs were 500, 250, 125, and 62.5ms, with a step size of 31.25ms. Each window had at least a 50% overlap with the previous one to make sure the activity on the edges of each window is not going to be neglected. We generated accuracy graphs for each window (Fig. 4) to visualize the areas where the classification was consistently more accurate than 70% (i.e. accuracy of training on that particular time-window and testing on the same time-window from the test data).

Covert Visuospatial Attention direction classification flowchart
Figure 4: Covert Visuospatial Attention direction classification flowchartView EqTD

We hypothesized that these periods can potentially provide better distinction between neural activities when the participant is covertly attending to the left vs. right. We named these regions High Distinction Periods (HDPs) and decided to only use the windows in the HDPs for training and classification to increase classifier performance.

We marked 75% of the Session 2 data and used the HDPs to train our classifiers. The remaining 25% of the data was not marked, and was used as test. The classifier then generated a string of lefts and rights (e.g. LLRLRRRLLRLRLRLL…) which was compared to our chart to determine the accuracy rate (Fig. 5). Due to a human error in recording, we had to discard the data from one of the participants, for the remaining 3 we were able to classify the unknown data (to the program) with at least 70% accuracy across different participants (70 to 74.07%). This indicated that our aim of classifying AV-CVSA direction with high accuracy across all participants is feasible, but needs further investigation and optimization with a larger sample. In our next steps we aim to increase the classification performance by optimizing the classifiers, assess the role of training the participants in the overall task performance, optimize the data collection and classification pipeline for real-time use, and test our prototype with ALS patients to further optimize the system to address their communication and control needs.

Covert Visuospatial Attention direction classification flowchart
Figure 5: Covert Visuospatial Attention direction classification flowchartView EqTD


  • University of Wisconsin-Milwaukee
    • Roger O. Smith, PhD, OT, FAOTA Resna Fellow
    • Wendy E. Huddleston, PT, PhD
    • Jun Zhang, PhD
    • Sabine Heuer, PhD, CCC-SLP
    • Maysam M. Ardehali, BS, PhD Candidate
    • Qussai M. Obiedat, MS, PhD Candidate
  • Marquette University
    • Sheikh Iqbal Ahamed, PhD
    • Olawunmi George, MS
  • National Center for Adaptive Neurotechnologies (NCAN)
    • Theresa Vaughan, BA
  • Medical College of Wisconsin
    • Edgar A. DeYoe, PhD
    • Adam S. Greenberg, PhD
  • Medical College of Wisconsin – ALS Clinic
    • Paul E. Barkhaus, MD, FAAN, FAANEM
    • Serena Thompson, MD, PhD
    • Dominic Fee, MD

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