Assistive technologies for severe and profound hearing loss: Beyond hearing aids and implants.

Assistive technologies (ATs) offer capabilities that were previously inaccessible to individuals with severe and profound hearing loss who have no or limited access to hearing aids and implants. This literature review aims to explore existing ATs and identify what still needs to be done. It is found that there is a lack of focus on the overall objectives of ATs. In addition, several other issues are identified, i.e. only a very small number of ATs developed within a research context have led to commercial devices, and there is a predisposition to use the latest expensive technologies and a tendency to avoid designing products universally. Finally, the further development of plug-ins that translate the text content of a website to various sign languages is needed to make information on the internet more accessible.

Interaction of participant characteristics and type of AAC with individuals with ASD: a meta-analysis.

Individuals with autism spectrum disorders (ASD) and complex communication needs often rely on augmentative and alternative communication (AAC) as a means of functional communication. This meta-analysis investigated how individual characteristics moderate effectiveness of three types of aided AAC: the Picture Exchange Communication System (PECS), speech-generating devices (SGDs), and other picture-based AAC. Effectiveness was measured via the Improvement Rate Difference. Results indicated that AAC has small to moderate effects on speech outcomes, and that SGDs appear to be most effective when considering any outcome measure with individuals with ASD without comorbid intellectual/developmental disorders (IDD). PECS appears to be most effective when considering any outcome measure with individuals with ASD and IDD. SGDs and PECS were the most effective type of AAC for preschoolers, when aggregating across outcome measures. No difference was found between systems for elementary-aged and older individuals.

Adaptive classification for brain computer interfaces.

In this paper we evaluate the performance of a new adaptive classifier for the use within a Brain Computer-Interface (BCI). The classifier can either be adaptive in a completely unsupervised manner or using unsupervised adaptation in conjunction with a neuronal evaluation signal to improve adaptation. The first variant, termed Adaptive Linear Discriminant Analysis (ALDA), updates mean values as well as covariances of the class distributions continuously in time. In simulated as well as experimental data ALDA substantially outperforms the non-adaptive LDA. The second variant, termed Adaptive Linear Discriminant Analysis with Error Correction (ALDEC), extends the unsupervised algorithm with an additional independent neuronal evaluation signal. Such a signal could be an error related potential which indicates when the decoder did not classify correctly. When the mean values of the class distributions circle around each other or even cross their way, ALDEC can yield a substantially better adaptation than ALDA depending on the reliability of the error signal. Given the non-stationarity of EEG signals during BCI control our approach might strongly improve the precision and the time needed to gain accurate control in future BCI applications.