Think to Speak - A Piezoelectric-EEG system for Augmentative and Alternative Communication (AAC) using Recurrent Neural Networks

Robert Sowah, Ryan Friedman, Abdul R. Ofoli, Baffour Sarkodie-Mensah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The collection of individuals with severe speech and physical impairments (SSPI), is the target audience for the Think to Speak Augmentative and Alternative Communication (AAC) system. The slow communication rate of AACs accessible to the target audience renders them undesirable, exhausting to operate, and a barrier to social and economic inclusion. This research synergizes the use of Electroencephalography (EEG) and high-sensitivity piezoelectric sensor readings with a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) to create a physically accessible AAC with performance comparable to 7.8 characters per minute communication rate. Since self-expression is inextricably linked with physical, mental, and emotional health, this research is of great significance to the estimated one percent of the global population with complex communication needs.

Original languageEnglish
Title of host publication2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538645390
DOIs
Publication statusPublished - Sep 2019
Event2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 - Baltimore
Duration: 29 Sep 20193 Oct 2019

Publication series

Name2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Conference

Conference2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
Country/TerritoryUnited States
CityBaltimore
Period29/09/193/10/19

Keywords

  • Complex Communication Needs
  • Electroencephalography (EEG)
  • Long Short-Term Memory
  • Piezoelectric sensor
  • Recurrent Neural Network

Fingerprint

Dive into the research topics of 'Think to Speak - A Piezoelectric-EEG system for Augmentative and Alternative Communication (AAC) using Recurrent Neural Networks'. Together they form a unique fingerprint.

Cite this