TOWARDS GAZE-INDEPENDENT C-VEP BCI: A PILOT STUDY

Introduction

A brain-computer interface (BCI) records its users’ brain activity and translates it into a computer command, opening a novel non-muscular channel for communication and control . Typically, a BCI records brain activity with electroencephalography (EEG) because it is affordable, practical, and non-invasive.

One of the fastest BCIs for communication uses the codemodulated visual evoked potential (c-VEP) as measured in the EEG .

As each of the presented symbols concurrently flickers with a random but unique sequence of flashes, specific brain activity is evoked when the user attends to one of the symbols.

Research Question

Our objective is to acquire fundamental insights on the feasibility of decoding the c-VEP in a fully gazeindependent manner.

Methodology

They reported a typing speed of 2.3-5 char\/min and 4.6-7.6 char\/min, for their two participants respectively. The authors reported an average performance of 70.3 %, 72.8 % and 79.5 % when using the SSVEP, alpha band, or both features in their analysis pipeline, respectively (N = 2 classes).

Study Design

Specifically, participants will use covert spatial attention to concentrate on stimuli, eliminating the need for direct eye movements to foveate on them.

In this pilot work, the stimuli were presented sequentially, to assess whether the c-VEP can be decoded from the far periphery, before testing the more complex parallel stimulation case, where stimuli would be presented simultaneously.

Results & Findings

The c-VEP is observed during visual stimulation of the user with a pseudo-random sequence of flashes. Subsequently, machine learning algorithms infer the attended symbol from the users’ evoked brain activity.

• The c-VEP is observed during visual stimulation of the user with a pseudo-random sequence of flashes.
• Subsequently, machine learning algorithms infer the attended symbol from the users’ evoked brain activity.
• This covert ‘Hexo-Spell’ outperformed the covert ‘Matrix’ speller, with a classification accuracy of 60 % (N = 36 classes) and 40 % (N = 30 classes).
• A classification accuracy of 91.3 %, 88.2 %, and 97.1 % was reported for the three spellers, respectively (N = 30 classes).
• Additionally, Treder and colleagues, in another instance, focused on using changes in alpha band activity induced by covert attention shifts to classify the direction in which.

Practical Applications

If successful, this study provides the first steps to a gaze-independent c-VEP BCI, potentially providing a high-speed neuro-technological assistive device for individuals who may not have reliable control of their eye movements. Finally, the data were downsampled to 120 Hz, and the 500 ms pre-stimulus that may have captured filter artefacts due to initial slicing and subsequently filtering were removed.

Inside the circles (3 \u2022 diameter), five colored shapes were presented with a maximum possible height and width of 1.4 \u2022 each.

Given the limited number of classes, there is potential to explore shorter codes, which could lead to faster decoding.

Figures Explained

The paper’s visual material highlights the workflow and the main system components.

• Figure 1 :: Figure1: Stimulus protocol. In (a), a graphical representation of the stimulus interface is depicted, featuring two stimuli positioned at 2.1 \u2022 on either side of a fixation cross. The stimuli took the form of circles measuring 3 \u2022 in both height and width. The fixation cross was 0.7 \u2022 for each side. The shapes presented were bound to a maximum height and width of 1.4 \u2022 each. The shapes’ heights and widths were as follows: green circle (1.4 \u2022 diameter), inverted cyan triangle and yellow triangle (0.9 \u2022 \u00d7 1.4 \u2022 ), magenta hourglass (0.9 \u2022 \u00d7 1.4 \u2022 ) and the red rectangle (1.5 \u2022 \u00d7 0.5 \u2022 , rotated by 45 \u2022 ). In (b), a graphical representation of the stimulus protocol is depicted comprising two crucial components: first, the background of the stimuli underwent alternating black-and-white transitions following a binary pseudo-random sequence; second, diverse-colored shapes were presented within the stimuli. The stimulus background could dynamically change with each frame of 16.67 ms (60 Hz), while the shapes within the stimuli changed every 250 ms (4 Hz). A trial took 20 s, within which target shapes (the magenta hour glass) appeared randomly in the sequence with at least 1 s distance. Participants engaged with the stimuli by counting the number of target shapes on the attended side. In this pilot study, we adopted a paradigm where only the background of the attended stimulus alternated, while the background of the unattended stimulus remained constant. A left-attended trial is shown in (b).
• Figure 3 :: Figure 3: Spatial activity pattern and transient responses of participant S4. (a) and (b) show the spatial activity pattern and transient responses of S4for the overt and covert conditions, respectively. For all participants, the spatial activity for the overt condition was more focally distributed as compared to the more lateralized distribution seen for the covert condition. The spatial pattern a \u2208 R C was estimated as a = w \u22a4 \u03a3 \u03a3 \u03a3, where \u03a3 \u03a3 \u03a3 \u2208 R C\u00d7C is the spatial covariance matrix.

Conclusion

The conclusion asserts the feasibility of a covert BCI design based on c-VEP, which is crucial for users without voluntary eye movement control. It suggests future research directions to enhance the efficacy of gaze-independent BCIs.