Human-machine cognitive coalescence through an internal duplex interface

In this thesis, we present a non-invasive and non-intrusive system that enables silent duplex human-machine communication and enables an interface that is internal to the user. We present a peripheral nerve-computer interface, AlterEgo, that allows a user to silently converse with a computing device without any voice or any discernible movements - thereby enabling the user to communicate with devices, AI assistants, applications or other people in a silent, concealed and seamless manner. A user's volitional internally articulated speech is characterized by efferent signal signatures in internal speech articulators that are captured and recognized by the proposed system. The hope is to facilitate a natural language user interface, where users can silently communicate in natural language and receive information and sensory input aurally through bone conduction. This enables a discreet, closed-loop interface with a computing device, and thus providing a seamless form of cognitive augmentation. The goal of the thesis is to describe the architecture, design, implementation and operation of the entire system along with demonstrating the utility of the platform as a personal computing system.

Department dc:contributor.department

Program in Media Arts and Sciences (Massachusetts Institute of Technology)

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Kapur, Arnav

Advisor dc:contributor.advisor

• Pattie Maes.

• Program in Media Arts and Sciences ()

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/120883

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/120883