Abstract
Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies.
Highlights
A Brain Computer Interface (BCI) enables a user to communicate with the external world by directly translating his/her brain activity into commands without relying on the brain’s normal output pathways
BCIs have raised great hopes in providing alternative communication means for persons suffering from motor disabilities such as amyotrophic lateral sclerosis (ALS), spinal cord injuries or brain paralysis [1,2,3], and other users targeted by the Ambient Assisted Living (AAL)
We describe recent developments and implementation of language models for BCI spellers
Summary
A Brain Computer Interface (BCI) enables a user to communicate with the external world by directly translating his/her brain activity into (computer) commands without relying on the brain’s normal output pathways. A BCI system in general (see Figure 1) normally comprises the following components: (i) a device that records the brain activity which is either invasive (e.g., electrocorticography) or non-invasive (e.g., electroencephalogram (EEG)); (ii) a preprocessor that reduces noise and artifacts, prepares the signals for further processing and extracts the relevant information from the recordings; (iii) a decoder that classifies the extracted relevant information into a control signal for (iv) an external device that could be any type BCI-compatible application (e.g., a robotic actuator, a prosthesis, a computer screen etc.), and that provides feedback to the user. The BCI can be regarded as a control system with active feedback (closed-loop system)
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