The RERC on AAC is a collaborative center committed to advancing knowledge and producing innovative engineering solutions in augmentative and alternative communication (AAC). The RERC on AAC will support a research and development program that addresses three areas of rehabilitation science and engineering:
- Improving access to technologies through exploration of innovative approaches and through integration of multi-modalities;
- Developing innovative language support technologies, including natural language processing and computer-mediation, to support effective communication for children and adults with limited access to language;
- Improving the human computer interface to reduce cognitive visual processing demands and enhance communication performance.
We also will support a range of training and dissemination activities. Our goal is that the AAC technologies and knowledge generated by the RERC on AAC will enable individuals with complex communication needs to achieve the basic human right of communication, and to maximize their participation in education, employment, health and community activities.
This webcast summarizes the positive results observed for research-based literacy interventions with individuals with ASD
The RERC on AAC is pleased to announce the 2018 Student Research and Design Competition
99 parents of children with ASD and limited speech and 211 SLPs who served children with ASD and limited speech completed questionnaires measuring their experiences with the provision of family-centered services.
Fernando Quivira and Matt Higger (Northeastern University) won the 2017 RERC on AAC Student Research and Design Competition
What advances have been made to date in AAC interventions for children? And what are the challenges that we need to address to improve outcomes?
“In this article, I will briefly discuss the population base of adults with acquired complex communication needs…”
Within AAC, switch scanning is a common paradigm for spelling where a set of characters is highlighted and the user is queried as to whether their target character is in the highlighted set. This work seeks a decision tree which requires the fewest expected queries per decision sequence (EQPD).
in this manuscript we propose a Kronecker product structure for covariance matrices
In this paper we propose a structure for the covariance matrices of the multichannel EEG signals.
In this manuscript, we develop a Monte Carlo-based probabilistic simulation framework for electroencephalography (EEG) based BCI design.