The Brain Computer Interface: A Review and Some New Concepts

Over the past decade, many laboratories have begun to explore brain computer interface (BCI) technology as a new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. This work outlines the potential benefits of BCI, summarises a number of developments which have been made in recent years and provides an overview of the fundamental requirements which must be acknowledged for the successful progression of BCI technology. A novel proposal for a unique BCI system is also detailed

Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements

FPGA-Based Discrete Wavelet Transforms System

Abstract. Although FPGA technology offers the potential of designing high performance systems at low cost, its programming model is prohibitively low level. To allow a novice signal/image processing end-user to benefit from this kind of devices, the level of design abstraction needs to be raised. This approach will help the application developer to focus on signal/image processing algorithms rather than on low-level designs and implementations. This paper presents a framework for an FPGA-based Discrete Wavelet Transform system. The approach helps the end-user to generate FPGA configurations for DWT at a high level without any knowledge of the low-level design styles and architectures.

The Brain Computer Interface: A Review and Some New Concepts

Over the past decade, many laboratories have begun to explore brain computer interface (BCI) technology as a new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. This work outlines the potential benefits of BCI, summarises a number of developments which have been made in recent years and provides an overview of the fundamental requirements which must be acknowledged for the successful progression of BCI technology. A novel proposal for a unique BCI system is also detailed