EEG amplitude modulation analysis for semi-automated diagnosis of Alzheimer's disease

Recent experimental evidence has suggested a neuromodulatory deficit in Alzheimer's disease (AD). In this paper, we present a new electroencephalogram (EEG) based metric to quantitatively characterize neuromodulatory activity. More specifically, the short-term EEG amplitude modulation rate-of-change (i.e., modulation frequency) is computed for five EEG subband signals. To test the performance of the proposed metric, a classification task was performed on a database of 32 participants partitioned into three groups of approximately equal size: healthy controls, patients diagnosed with mild AD, and those with moderate-to-severe AD. To gauge the benefits of the proposed metric, performance results were compared with those obtained using EEG spectral peak parameters which were recently shown to outperform other conventional EEG measures. Using a simple feature selection algorithm based on area-under-the-curve maximization and a support vector machine classifier, the proposed parameters resulted in accuracy gains, relative to spectral peak parameters, of 21.3% when discriminating between the three groups and by 50% when mild and moderate-to-severe groups were merged into one. The preliminary findings reported herein provide promising insights that automated tools may be developed to assist physicians in very early diagnosis of AD as well as provide researchers with a tool to automatically characterize cross-frequency interactions and their changes with disease.

Clinical Efficacy of a New Automated Hemoencefalographic Neurofeedback Protocol

Among the ongoing attempts to enhance cognitive performance, an emergent and yet underrepresented venue is brought by hemoencefalographic neurofeedback (HEG). This paper presents three related advances in HEG neurofeedback for cognitive enhancement: a) a new HEG protocol for cognitive enhancement, as well as b) the results of independent measures of biological efficacy (EEG brain maps) extracted in three phases, during a one year follow up case study; c) the results of the first controlled clinical trial of HEG, designed to assess the efficacy of the technique for cognitive enhancement of an adult and neurologically intact population. The new protocol was developed in the environment of a software that organizes digital signal algorithms in a flowchart format. Brain maps were produced through 10 brain recordings. The clinical trial used a working memory test as its independent measure of achievement. The main conclusion of this study is that the technique appears to be clinically promising. Approaches to cognitive performance from a metabolic viewpoint should be explored further. However, it is particularly important to note that, to our knowledge, this is the world's first controlled clinical study on the matter and it is still early for an ultimate evaluation of the technique.

Automated system of seedling image analysis (SVIS) and electrical conductivity to assess sun hemp seed vigor

The development of new procedures for quickly obtaining accurate information on the physiological potential of seed lots is essential for developing quality control programs for the seed industry. In this study, the effectiveness of an automated system of seedling image analysis (Seed Vigor Imaging System - SVIS) in determining the physiological potential of sun hemp seeds and its relationship with electrical conductivity tests, were evaluated. SVIS evaluations were performed three and four days after sowing and data on the vigor index and the length and uniformity of seedling growth were collected. The electrical conductivity test was made on 50 seed replicates placed in containers with 75 mL of deionised water at 25 ºC and readings were taken after 1, 2, 4, 8 and 16 hours of imbibition. Electrical conductivity measurements at 4 or 8 hours and the use of the SVIS on 3-day old seedlings can effectively detect differences in vigor between different sun hemp seed lots.