# An algorithm for onset detection of linguistic segments in continuous electroencephalogram signals

arXiv: https://arxiv.org/abs/2012.06075 · HTML: https://mlsanity.com/paper/2012.06075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals](https://mlsanity.com/paper/2105.04294/) (2021)

## Abstract (arXiv)

A Brain Computer Interface based on imagined words can decode the word a subject is thinking on through brain signals to control an external device. In order to build a fully asynchronous Brain Computer Interface based on imagined words in electroencephalogram signals as source, we need to solve the problem of detecting the onset of the imagined words. Although there has been some research in this field, the problem has not been fully solved. In this paper we present an approach to solve this problem by using values from statistics, information theory and chaos theory as features to correctly identify the onset of imagined words in a continuous signal. On detecting the onsets of imagined words, the highest True Positive Rate achieved by our approach was obtained using features based on the generalized Hurst exponent, this True Positive Rate was 0.69 and 0.77 with a timing error tolerance region of 3 and 4 seconds respectively.