|1||2013-10-08||An auditory brain–computer interface evoked by natural speech||Younghak Shin||PPT|
|In this study, they present a novel fully auditory EEG-BCI based on a dichotic listening paradigm using human voice for stimulation. This interface has been evaluated with healthy volunteers, achieving an average information transmission rate of 1.5 bits min−1 in full-length trials and 2.7 bits min−1 using the optimal length of trials, recorded with only one channel and without formal training. This
novel technique opens the door to a more natural communication with users unable to use visual BCIs, with promising results in terms of performance, usability, training and cognitive effort.
|2||2013-10-15||Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface||Evgenii||ppt|
In this paper, they describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. They used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left hand and right-hand motor imagery. They applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right hand imagery with an average accuracy of 73% for all volunteers, while HMM performed better with an average accuracy of 89%. Their results indicate potential application of NIRS in the development of BCIs.
|3||2013-10-22||A measurement-domain beamforming approach for ultrasound instrument based on distributed compressed sensing: initial development||Pavel Nee||ppt|
|In this paper author applied distributed compressed sensing to ultrasound medical imaging and proposed Measurement-domain adaptive beamforming (MABF) to directly reconstruct ultrasound image without reconstructing transducer signals.|
|4||2013-10-29||A novel BCI based on ERP components sensitive to configural processing of human faces||Seungchan Lee||(pdf)|
|This study introduces a novel brain–computer interface (BCI) based on an oddball paradigm using stimuli of facial images with loss of configural face information (e.g., inversion of face). With the proposed novel paradigm, we investigate the effects of ERP components N170, VPP and P300 on target detection for BCI. An eight-class BCI platform is developed to analyze ERPs and evaluate the target detection performance using linear discriminant analysis without complicated feature extraction processing. The online classification accuracy of 88.7% and information transfer rate of 38.7 bits min−1 using stimuli of inverted faces with only single trial.|