Data mining an EEG dataset with an emphasis on dimensionality reduction

The human brain is obviously a complex system, and exhibits rich spatiotemporal dynamics. Among the non-invasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution. Early attempts to analyse EEG data relied on visual inspection of EEG records. Since the introduction of EEG recordings, the volume of data generated from a study involving a single patient has increased exponentially. Therefore, automation based on pattern classification techniques have been applied with considerable success. In this study, a multi-step approach for the classification of EEG signal has been adopted. We have analysed sets of EEG time series recording from healthy volunteers with open eyes and intracranial EEG recordings from patients with epilepsy during ictal (seizure) periods. In the present work, we have employed a discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time - that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. Principal components analysis (PCA) and rough sets have been used to reduce the data dimensionality. A multi-classifier scheme consists of LVQ2.1 neural networks have been developed for the classification task. The experimental results validated the proposed methodology

Optimization Studies for the Gas Atomization and Selective Laser Melting Processes of Al10SiMg alloy

Selective laser melting (SLM) is an additive manufacturing technology that can fabricate complex engineering components using a scanning laser beam to melt consecutive layers of powders with characteristics that significantly influence material properties. Present work investigates both the gas atomization and SLM processes for the Al10SiMg alloy with a focus on establishing the relationships among atomization parameters, powder characteristics, SLM parameters and materials properties. Al10SiMg alloy powders (Al-10wt.%Si-0.5wt.%Mg) were batch-produced through gas atomization by systematically varying the melt flow rate (0.012 - 0.037 kg/s), gas pressure (1.4 - 3.1 MPa), and melt temperature (850C -1000C). The highest yield of 80 wt.% was accomplished for powders with particle size smaller than 75um, considered suitable for SLM, utilizing gas pressure of 2.7 MPa, melt flow rate of 0.020 kg/s, and melt temperature of 950C. Investigations for the SLM process were carried out to identify the optimal particle size distribution (PSD) and critical reuse limit for Al10SiMg powders. Five distribution ranges ( \u3c 45µm, 20µm \u3c x \u3c 63µm, \u3c 75µm, \u3c 106µm, 75µm \u3c x \u3c 106µm), and five sets of recycled powder (new, one, five, ten and over twenty uses) were used to build SLM samples for metallographic and mechanical characterization. Archimedes\u27 method, optical, scanning electron microscopy and mechanical testing in tension were employed to assess the influence of powder feedstock on part density, microstructure and mechanical properties, respectively. All PSDs examined in this study produced samples with over 99% relative density, but samples built with size range of 75µm \u3c x \u3c 106µm yielded the highest tensile and yield strengths of 448 MPa and 265 MPa, respectively. Results from recycling demonstrated that Al10SiMg alloy powders can be reused in SLM without sacrificing quasi-static tensile properties