Affective Man-Machine Interface: Unveiling human emotions through biosignals

As is known for centuries, humans exhibit an electrical profile. This profile is altered through various psychological and physiological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electrodermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such a MMI requires the correct classification of biosignals to emotion classes. This chapter starts with an introduction on biosignals for emotion detection. Next, a state-of-the-art review is presented on automatic emotion classification. Moreover, guidelines are presented for affective MMI. Subsequently, a research is presented that explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 21 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31% correct classification of the four emotion classes, without the need of personal profiles. Among various other directives for future research, the results emphasize the need for parallel processing of multiple biosignals

Partial Encryption of Compressed Images Employing FPGA

In this paper, we present the realization of of partial encryption of compressed images on Altera FLEX10K FPGA device that allows for efficient hardware implementation. The compression algorithm decomposes images into several different parts. A secure encryption algorithm is then used to encrypt only the crucial parts, which are considerably smaller than the original image. This will result in significant reduction in processing time and computational requirement for encryption and decryption. The breadth-first traversal linear lossless quadtree decomposition method is used for the partial compression and RSA is used for the encryption. Functional simulations are carried out to verify the functionality of the individual modules and the system on four different images. Comparisons, verification and analysis made validate the advantage of this approach. The design has utilized 2928 units of LC and a system frequency of 13.42 MHz

Multimodal EEG and Keystroke Dynamics Based Biometric System Using Machine Learning Algorithms

Electroencephalography (EEG) based biometric systems are gaining attention for their anti-spoofing capability but lack accuracy due to signal variability at different psychological and physiological conditions. On the other hand, keystroke dynamics-based systems achieve very high accuracy but have low anti-spoofing capability. To address these issues, a novel multimodal biometric system combining EEG and keystroke dynamics is proposed in this paper. A dataset was created by acquiring both keystroke dynamics and EEG signals simultaneously from 10 users. Each user participated in 500 trials at 10 different sessions (days) to replicate real-life signal variability. A machine learning classification pipeline is developed using multi-domain feature extraction (time, frequency, time-frequency), feature selection (Gini impurity), classifier design, and score level fusion. Different classifiers were trained, validated, and tested for two different classification experiments-personalized and generalized. For identification and authentication, 99.9% and 99.6% accuracies are achieved, respectively for the Random Forest classifier in 5 fold cross-validation. These results outperform the individual modalities with a significant margin (5%). We also developed a binary template matching-based algorithm, which gives 93.64% accuracy 6X faster. The proposed method can be considered secure and reliable for any kind of biometric identification and authentication.Scopu

VHDL Modeling for Classification of Power Quality Disturbance Employing Wavelet Transform, Artificial Neural Network and Fuzzy Logic

The identification and classification of voltage and current disturbances are important tasks in the monitoring and protection of power systems. Most power quality disturbances are non-stationary and transitory and both detection and classification have proved to be very demanding. New intelligent system technologies that use wavelet transforms, expert systems and artificial neural networks include some unique advantages regarding fault analysis. This paper presents a new approach to classifying six classes of signals: five types of disturbance including sag, swell, transient, fluctuation, interruption, and the normal waveform. The concept of discrete wavelet transform for feature extraction from the power disturbance signal, combined with an artificial neural network and incorporating fuzzy logic to offer a powerful tool for detecting and classifying power quality problems, is introduced. The system was modeled using VHDL, a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. The extensive simulation results confirm the feasibility of the proposed algorithm. This method proposed herein classified and obtained 98.19% classification accuracy from the application of this system to software-generated signals and utility sampled disturbance events