Portable Brain Computer Interface (BCI) in the Intensive Care Unit (ICU)

Brain-computer interface;Steady-state visual evoked potential;Canonical correlation analysis;Fusion;Power spectral density analysis;Dimensionality reduction;Discriminative transformation;Computer science;Computer and Information Science, College of Engineering & Computer Science

Farooq, Muhamed K.Bacha, Anys ;

University of Michigan

关键词: Brain-computer interface;    Steady-state visual evoked potential;    Canonical correlation analysis;    Fusion;    Power spectral density analysis;    Dimensionality reduction;    Discriminative transformation;    Computer science;    Computer and Information Science, College of Engineering & Computer Science;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/143184/Portable%20Brain%20Computer%20Interface%20%28BCI%29%20in%20the%20Intensive%20Care%20Unit%20%28ICU%29%20%281%29.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Steady State Visual Evoked Potentials (SSVEPs) have been the most commonly utilizedBrain Computer Interface (BCI) modality due to their relatively high signal-to-noise ratio,high information transfer rates, and minimum training prerequisites. Up to date Canonical Correlation Analysis (CCA) and its extensions have been widely utilized for SSVEP targetfrequency identification. However, reliable and robust SSVEP identification performance is still a challenge, particularly for portable BCI systems operating in an Intensive Care Unit (ICU) department filled with various source of noise. As such, I propose an innovative partition-based feature extraction method that entails partitioning the score spaces of CCAand Power Spectral Density Analysis (PSDA) in three cases, extract efficient descriptorsfrom each partition, then concatenate the extracted measures to generate morediscriminative fusion spaces. Moreover, I investigate transforming the fusion spaces to lower dimensions utilizing Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, to validate the proposed method, I compare the performance ofthe partition-based feature extraction and score space fusion method to a well-establishedSSVEP identification method based on Multivariate Linear Regression (MLR). Theexperimental results of this investigation report that the proposed method enhances the identification performance of the CCA-based BCI system from 63% to 78%. Theidentification performance is further improved to 98% after the discriminative transformation with LDA outperforming MLR, which achieved an average overall 86% identification accuracy. As such, the proposed method is a promising approach to implement and operate BCI systems in the ICU.