A preliminary study of steady-state visually-evoked potential-based non-invasive brain-computer interface technology as a communication aid for patients with amyotrophic lateral sclerosis

Abstract

Background: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects motor neurons, leading to severe disability and ultimately death. Communication difficulties are common in ALS patients as the disease progresses; thus, alternative communication aids need to be explored. This study sought to examine the use and effect of steady-state visually-evoked potential (SSVEP)-based non-invasive brain-computer interface (BCI) technology as a communication aid for patients with ALS and to examine possible influencing factors.

Methods: In total, 12 patients with ALS were selected, and a 40-character target selection was performed using SSVEP-based non-invasive BCI technology. The patients were presented with specific visual stimuli, and nine-lead electroencephalogram (EEG) signals in the occipital region were acquired when the patients were looking at the target. Using the feature recognition analysis method, the final output was the characters recognized by the patients. The basic clinical data of the patients (e.g., age, gender, course of disease, affected area, and ALS functional scale score) were collected, and the BCI accuracy rate, information transmission rate, and average SSVEP recognition time were calculated.

Results: The results revealed that the recognition efficiency of the ALS patients varied. The accuracy potential increased as the stimulus duration extended, highlighting the possibility for improvement via further optimization. The results also showed that the experimental design schedules typically used for healthy individuals may not be entirely suitable for ALS patients, which presents an exciting opportunity to tailor future studies to better meet the unique needs of ASL patients. Further, the results revealed the necessity of using customized experimental schedules in future studies, which could lead to more relevant and effective data collection for ALS patients.

Conclusions: The study found that SSVEP-based non-invasive BCI technology has promising potential as a communication aid for ALS patients. While further algorithm optimization and comprehensive studies with larger sample sizes are necessary, the initial findings are encouraging, and could lead to the development of more effective communication solutions that are specifically tailored to address the challenges faced by ALS patients.

Keywords: Amyotrophic lateral sclerosis (ALS); non-invasive brain-computer interface technology (non-invasive BCI technology); steady-state visually-evoked potentials (SSVEPs).

Figure 1

The STE-DW algorithm flowchart. EEG, electroencephalogram; STE-DW, spatiotemporal equalization dynamic window.

Figure 2

The experimental paradigm stimulus flow chart (single trial). The red block indicates the target position. The blue box indicates the feedback stage results.

Figure 3

Electrode positions used for the data analysis (red mark). AF, anterior frontal; C, central; CP, central parietal; Cz, central zero; F, frontal; FC, frontal central; FCz, frontal central zero; FP, frontal pole; FPz, frontal pole zero; FT, frontal temporal; Fz, frontal zero; O, occipital; Oz, occipital zero; P, parietal; PO, posterior occipital; POz, posterior occipital zero; Pz, parietal zero; T, temporal; TP, temporal parietal.

Figure 4

The SNRs of the SSVEP responses of all patients (n=12 patients, S1–S12). SNR, signal-to-noise ratio; SSVEPs, steady-state visually-evoked potentials.

Figure 5

The SSVEP recognition accuracy rate and ITR of the 12 ALS patients (S1–S12). (A) The average recognition accuracy among all participants; (B) the average information transfer rate across all participants. ALS, amyotrophic lateral sclerosis; ITR, information transmission rate; SSVEPs, steady-state visually-evoked potentials.

Figure 6

The STE-DW algorithm was used to calculate the average ITR and average accuracy rate of all patients across different threshold conditions. (A) The average ITR. (B) The average accuracy rate. Each point in the figure represents the detection performance under different threshold “ϵ” conditions, where the circle indicates the detection performance under the condition of “ϵ =10⁻⁶”. ITR, information transmission rate; STE-DW, spatiotemporal equalization dynamic window.

Figure 7

Distribution of trial duration of SSVEPs in ALS patients (n=12 patients, S1–S12). ALS, amyotrophic lateral sclerosis; SSVEPs, steady-state visually-evoked potentials.

Figure 8

The recognition accuracy rate, ITR, and trial duration statistics of all patients (S1–S12). The dashed lines from the bottom to the top of the figure indicate the relationship between the ITR and recognition accuracy rate under the conditions of t =2, 2.4, 2.8, and 3.2 seconds. The closer to the yellow curve, the longer the average time of the patients, and the closer to the purple curve, the shorter the single trial time of the patients. ITR, information transmission rate; t, time.