An Auditory Paradigm for Brain-Computer Interfaces

Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged event-related potentials, we show that an untrained user's EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI

Attentional Modulation of Auditory Event-Related Potentials in a Brain-Computer Interface

Motivated by the particular problems involved in communicating with "locked-in" paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged event-related potentials, we show that an untrained user‘s EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI

Attentional modulation of auditory event-related potentials in a brain-computer interface

Motivated by the particular problems involved in communicating with “locked-in ” paralysed patients, we aim to develop a brain-computer interface that uses auditory stimuli. We describe a paradigm that allows a user to make a binary decision by focusing attention on one of two concurrent auditory stimulus sequences. Using Support Vector Machine classification and Recursive Channel Elimination on the independent components of averaged event-related potentials, we show that an untrained user's EEG data can be classified with an encouragingly high level of accuracy. This suggests that it is possible for users to modulate EEG signals in a single trial by the conscious direction of attention, well enough to be useful in BCI

Usefulness as the Criterion for Evaluation of Interactive Information Retrieval

The purpose of an information retrieval (IR) system is to help users accomplish a task. IR system evaluation should consider both task success and the value of support given over the entire information seeking episode. Relevance-based measurements fail to address these requirements. In this paper, usefulness is proposed as a basis for IR evaluation

Usefulness as the Criterion for Evaluation of Interactive Information Retrieval

The purpose of an information retrieval (IR) system is to help users accomplish a task. IR system evaluation should consider both task success and the value of support given over the entire information seeking episode. Relevance-based measurements fail to address these requirements. In this paper, usefulness is proposed as a basis for IR evaluation