Archive | 2019
EEG Vowel Silent Speech Signal Discrimination Based on APIT-EMD and SVD
Abstract
A Brain-Computer Interface (BCI) System captures the neural activity of the Central Nervous System (CNS) and delivers an output which replaces the natural output of the CNS [1]. That helps who have lost their ability to speak, spelling words in a monitor or helps to recover the movements for people who have suffered some amputation of their limbs or a motor paralysis of their body. The main objective of the project is to control an upper limb using a Myohand twin Ottobock prosthesis ref 8E38 = 7 [2] using EEG silent speech signals. On this research, we will focus on a novel methodology that attempts to classify imagined speech based on vowels, which uses as the primary technique for artifact removal the Adaptive-Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition (APIT-MEMD) [3], and for the feature generation stage the Singular Value Decomposition (SVD) [4]. For the classification stage, two classifiers where tested, the Extremely Randomized Trees classifier (ET) [5], and the Adaboost (ADB) [6]. The overall accuracy achieved per subject and per vowels’ pairwise classification, was 91.54% using ET. For a multiclass classifier, the overall accuracy over the eighteen subjects of the database was 79.06%.