Online classification of imagined speech using functional near-infrared spectroscopy signals.

OBJECTIVE: Most brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS) require that users perform mental tasks such as motor imagery, mental arithmetic, or music imagery to convey a message or to answer simple yes or no questions. These cognitive tasks usually have no direct association with the communicative intent, which makes them difficult for users to perform. APPROACH: In this paper, a 3-class intuitive BCI is presented which enables users to directly answer yes or no questions by covertly rehearsing the word 'yes' or 'no' for 15 s. The BCI also admits an equivalent duration of unconstrained rest which constitutes the third discernable task. Twelve participants each completed one offline block and six online blocks over the course of two sessions. The mean value of the change in oxygenated hemoglobin concentration during a trial was calculated for each channel and used to train a regularized linear discriminant analysis (RLDA) classifier. MAIN RESULTS: By the final online block, nine out of 12 participants were performing above chance (p < 0.001 using the binomial cumulative distribution), with a 3-class accuracy of 83.8% ± 9.4%. Even when considering all participants, the average online 3-class accuracy over the last three blocks was 64.1 % ± 20.6%, with only three participants scoring below chance (p < 0.001). For most participants, channels in the left temporal and temporoparietal cortex provided the most discriminative information. SIGNIFICANCE: To our knowledge, this is the first report of an online 3-class imagined speech BCI. Our findings suggest that imagined speech can be used as a reliable activation task for selected users for development of more intuitive BCIs for communication.

Analysis of Different Classification Techniques for Two-Class Functional Near-Infrared Spectroscopy-Based Brain-Computer Interface.

We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks. In the classification, six different modalities, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbour (kNN), the Naïve Bayes approach, support vector machine (SVM), and artificial neural networks (ANN), were utilized. With these classifiers, the average classification accuracies among the seven subjects for the 2- and 3-dimensional combinations of features were 71.6, 90.0, 69.7, 89.8, 89.5, and 91.4% and 79.6, 95.2, 64.5, 94.8, 95.2, and 96.3%, respectively. ANN showed the maximum classification accuracies: 91.4 and 96.3%. In order to validate the results, a statistical significance test was performed, which confirmed that the p values were statistically significant relative to all of the other classifiers (p < 0.005) using HbO signals.

Vector-based phase classification of initial dips during word listening using near-infrared spectroscopy.

This study examined the classification of initial dips during passive listening to single words by analysis of vectors of deoxyHb and oxyHb measurements simultaneously derived from near-infrared spectroscopy. The initial dip response during a single-word 1.5-s task in 13 healthy participants was significant only in the language area, which includes the left posterior superior temporal gyrus and angular gyrus. Event-related vectors of responses to comprehended words moved significantly into phase 4, a dip phase, whereas vectors of responses to unknown words moved into a nondip phase (P<0.05). The same results were reproduced after previously unknown words were learnt by the participants. Among the five dip phases, reflecting variations in transient oxygen metabolic regulation during a task, the frequency of occurrence of hypoxic-ischemic initial dips (decreased oxyHb) was around three times that of the canonical dip (increased deoxyHb and oxyHb). Phase classification of event-related vectors enhances the slight amount of oxygen exchange that occurs in word recognition, which has been difficult to detect because of its small amplitude.

Medical devices; neurological devices; classification of the Near Infrared Brain Hematoma Detector. Final rule.

The Food and Drug Administration (FDA) is classifying the Near Infrared (NIR) Brain Hematoma Detector into class II (special controls). The Agency is classifying the device into class II (special controls) in order to provide a reasonable assurance of safety and effectiveness of the device.

Preparation and characterization of 5-fu loaded microspheres of eudragit and ethylcellulose

In the present investigation, 5-fluorouracil loaded microspheres of Eudragit (RS 100, RL 100 and RSPO) and ethylcellulose were prepared. “O/O solvent evaporation” technique was used for preparation of microspheres using (methanol + acetone)/liquid paraffin system. Magnesium stearate was used as the droplet stabilizer and n-hexane was added to harden the microspheres. The prepared microspheres were characterized for their micromeretic properties and entrapment efficiency; as well by Fourier transform infrared spectroscopy (FTIR) and thin layer chromatography (TLC). Photomicrographs were taken to study the shape of microspheres. The best fit release kinetics was achieved with Higuchi plot. Mean particle size, entrapment efficiency and production yields were highly influenced by the type of polymer and polymer concentration. It is concluded from the present investigation that various Eudragit and Ethylcellulose are promising controlled release carriers for 5-FU(AU)

En la presente investigación, se han preparado microesferas de Eudragit (RS 100, RL 100 y RSPO) y etilcelulosa cargadas con 5-fluorouracilo. Se ha utilizado la técnica de “evaporación del disolvente o/o” para preparar las microesferas utilizando el sistema de (metanol + acetona)/ parafina líquida. Se ha utilizado estearato de magnesio como estabilizador de gotículas y se ha añadido n-hexano para endurecer las microesferas. Las microesferas preparadas se caracterizan por sus propiedades micromeríticas y su eficaz compresión, así como a través de la espectroscopia infrarroja transformada de Fourier (FTIR, por sus siglas en inglés) y la cromatografía en capa fina. Se han tomado microfotografías para estudiar la forma de las microesferas. La mejor cinética de liberación se ha alcanzado con el modelo de Higuchi. El tamaño de la partícula principal, la eficacia de compresión y los rendimientos de producción han sido fuertemente influenciados por el tipo de polímero y su concentración. Tras la presente investigación, se ha determinado que el Eudragit y la Etilcelulosa tienen carreras muy prometedoras en la liberación controlada para el 5-FU(AU)