Abstract
Brain–computer interfaces (BCIs) based on code-modulated visual evoked potentials (c-VEPs) typically utilize a synchronous approach to identify targets (i.e., after preset time periods the system produces command outputs). Hence, users have only a limited amount of time to fixate a desired target. This hinders the usage of more complex interfaces, as these require the BCI to distinguish between intentional and unintentional fixations. In this article, we investigate a dynamic sliding window mechanism as well as the implementation of software-based stimulus synchronization to enable the threshold-based target identification for the c-VEP paradigm. To further improve the usability of the system, an ensemble-based classification strategy was investigated. In addition, a software-based approach for stimulus on-set determination is proposed, which allows for an easier setup of the system, as it reduces additional hardware dependencies. The methods were tested with an eight-target spelling application utilizing an n-gram word prediction model. The performance of eighteen participants without disabilities was tested; all participants completed word- and sentence spelling tasks using the c-VEP BCI with a mean information transfer rate (ITR) of 75.7 and 57.8 bpm, respectively.
Highlights
A brain–computer interface (BCI) records, analyzes and interprets brain activity of the user and can be used for communication with the external environment, without involving muscle activity [1].BCIs can be utilized as communication device for severely impaired people; e.g., people suffering from spinal cord injuries, brain stem strokes, amyotrophic lateral sclerosis (ALS), or muscular dystrophies [2].If used as a spelling device, character output speed and classification accuracy are the most important characteristics of the system.Code-modulated visual evoked potentials (c-VEPs) have gathered increasing research interest in the field of Brain–Computer Interfaces (BCIs) [3,4,5,6]
We presented a dictionary-driven c-VEP spelling application utilizing n-gram based dictionary suggestions
Implementation of flexible time windows were realized, which are rarely seen in c-VEP systems, where typically fixed time windows are used
Summary
A brain–computer interface (BCI) records, analyzes and interprets brain activity of the user and can be used for communication with the external environment, without involving muscle activity [1]. Code-modulated visual evoked potentials (c-VEPs) have gathered increasing research interest in the field of Brain–Computer Interfaces (BCIs) [3,4,5,6]. Stimulus onset markers are typically sent to the EEG hardware These timestamps can be acquired using a photo-resistor or photo-diode attached to the screen [4,8]. Typical use cases of c-VEP BCIs are spelling applications for people with severe disabilities [9]. For these implementations, high classification accuracy and speed are desired. Implementation of a novel software-based synchronization between stimulus presentation and EEG data acquisition, investigation of performance improvements in c-VEP detection utilizing an ensemble-based classification approach, presenting dynamic on-line classification utilizing sliding classification windows and n-gram word prediction. The article evaluates the feasibility of the proposed methods based on a test with healthy participants
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