Ionic homeostasis, biochemical components and yield of Italian zucchini under nitrogen forms and salt stress

This research was carried out aiming at evaluating the effects of nitrate and ammonium ions on nutrient accumulation, biochemical components and yield of Italian zucchini (cv. Caserta) grown in a hydroponic system under salt stress conditions. The experiment was carried out in a greenhouse utilizing an experimental design in randomized blocks, arranged in a 2 x 5 factorial scheme, with 4 replications. The treatments consisted of two forms of nitrogen (nitrate - NO3- and ammonium - NH4+) and 5 electrical conductivity levels of irrigation water (ECw) (0.5, 2.0, 3.5, 5.0 and 6.5 dS m-1). The analysis of the results indicated that supply of N exclusively in NH4+ form promotes greater damage to the leaf membrane and reduction in accumulation of macronutrients and higher Na+/K+, Na+/Ca++ and Na+/Mg++ ratios in the shoots of zucchini plants. Electrical conductivity of irrigation water above 2.0 dS m-1 reduces the accumulation of nutrients in shoot and yield of Italian zucchini plant. The toxicity of NH4+ under Italian zucchini plants overlap the toxicity of the salinity, since its fertilization exclusively with this form of nitrogen inhibits its production, being the NO3- form the most suitable for the cultivation of the species.

Optimization of electrode channels in Brain Computer Interfaces

What is the optimal number of electrodes one can use in discrimination of tasks for a Brain Computer Interface (BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The systematic analysis results in the most reliable procedure in electrode optimization as well as a validating means for the other feature selection techniques. We acquired human scalp EEG in response to cue-based motor imagery tasks. We employed a systematic analysis by using all possible combinations of the channels and calculating task discrimination errors for each of these combinations by using linear discriminant analysis (LDA) for feature classification. Channel combination that resulted in the smallest discrimination error was selected as the optimum number of channels to be used in BCI applications. Results from the systematic analysis were compared with another feature selection algorithm: forward stepwise feature selection combined with LDA feature classification. Our results demonstrate the usefulness of the fully optimized technique for a reliable selection of scalp electrodes in BCI applications.

Feature selection on movement imagery discrimination and attention detection

Noninvasive brain-computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar ratios (PRs) were extracted from the EEG data for movement imagery discrimination. Event-related potentials (ERPs) were employed in the discrimination of cue-evoked responses. While center-out arrows, commonly used in calibration sessions, cued the subjects in the first experiment (for both PR and ERP analyses), less stimulating arrows that were centered in the visual field were employed in the second experiment (for ERP analysis). The proposed algorithms outperformed other three popular feature selection algorithms in movement imagery discrimination. In the first experiment, both algorithms achieved classification errors as low as 12.5% reducing the feature set dimensionality by more than 90%. The classification accuracy of ERPs dropped in the second experiment since centered cues reduced the amplitude of cue-evoked ERPs. The two proposed algorithms effectively reduced feature dimensionality while increasing movement imagery discrimination and detected cue-evoked ERPs that reflect subject attention.

3D electrode localization on wireless sensor networks for wearable BCI

This paper presents a solution for electrode localization on wearable BCI radio-enabled electrodes. Electrode positioning is a common issue in any electrical physiological recording. Although wireless node localization is a very active research topic, a precise method with few centimeters of range and a resolution in the order of millimeters is still to be found, since far-field measurements are very prone to error. The calculation of 3D coordinates for each electrode is based on anchorless range-based localization algorithms such as Multidimensional Scaling and Self-Positioning Algorithm. The implemented solution relies on the association of a small antenna to measure the magnetic field and a microcontroller to each electrode, which will be part of the wireless sensor network module. The implemented solution is suitable for EEG applications, namely the wearable BCI, with expected range of 20 cm and resolution of 5 mm.

Model-based responses and features in Brain Computer Interfaces

Novel model based features are introduced in the discrimination of motor imagery tasks using human scalp electroencephalography (EEG) towards the development of Brain Computer Interfaces (BCI). We have acquired human scalp EEG under open-loop and feedback conditions in response to cue-based motor imagery tasks. EEG signals, transformed into frequency specific bands such as mu, beta and movement related potentials, were used for feature extraction with the aim to discriminate tasks. Data were classified using features such as power spectrum and model-based parameters. Two different feature selection methods: stepwise and principal component analysis (PCA), were combined with linear discriminant analysis (LDA). Different training/validation criteria were applied for classification of task related features. Results show that the scalp EEG correlate of the imagery tasks of hands/toes/tongue movements under open-loop conditions and left/right hand movements under feedback conditions, can be well discriminated with classification errors below 20%. Model based techniques, which resulted in classification errors in the range of 2%-30%, have the potential to use advanced control systems theory in the development of BCI to achieve improved performance compared to the performance achieved by currently applied proportional control or filter algorithms.