7 research outputs found
Hyperdimensional Computing-based Multimodality Emotion Recognition with Physiological Signals
To interact naturally and achieve mutual sympathy between humans and machines, emotion recognition is one of the most important function to realize advanced human-computer interaction devices. Due to the high correlation between emotion and involuntary physiological changes, physiological signals are a prime candidate for emotion analysis. However, due to the need of a huge amount of training data for a high-quality machine learning model, computational complexity becomes a major bottleneck. To overcome this issue, brain-inspired hyperdimensional (HD) computing, an energy-efficient and fast learning computational paradigm, has a high potential to achieve a balance between accuracy and the amount of necessary training data. We propose an HD Computing-based Multimodality Emotion Recognition (HDC-MER). HDCMER maps real-valued features to binary HD vectors using a random nonlinear function, and further encodes them over time, and fuses across different modalities including GSR, ECG, and EEG. The experimental results show that, compared to the best method using the full training data, HDC-MER achieves higher classification accuracy for both valence (83.2% vs. 80.1%) and arousal (70.1% vs. 68.4%) using only 1/4 training data. HDC-MER also achieves at least 5% higher averaged accuracy compared to all the other methods in any point along the learning curve
Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata
In this paper, a hardware-optimized approach to emotion recognition based on
the efficient brain-inspired hyperdimensional computing (HDC) paradigm is
proposed. Emotion recognition provides valuable information for human-computer
interactions, however the large number of input channels (>200) and modalities
(>3) involved in emotion recognition are significantly expensive from a memory
perspective. To address this, methods for memory reduction and optimization are
proposed, including a novel approach that takes advantage of the combinatorial
nature of the encoding process, and an elementary cellular automaton. HDC with
early sensor fusion is implemented alongside the proposed techniques achieving
two-class multi-modal classification accuracies of >76% for valence and >73%
for arousal on the multi-modal AMIGOS and DEAP datasets, almost always better
than state of the art. The required vector storage is seamlessly reduced by 98%
and the frequency of vector requests by at least 1/5. The results demonstrate
the potential of efficient hyperdimensional computing for low-power,
multi-channeled emotion recognition tasks
Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications
This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments
Hyperdimensional Computing-based Multimodality Emotion Recognition with Physiological Signals
To interact naturally and achieve mutual sympathy between humans and machines, emotion recognition is one of the most important function to realize advanced human-computer interaction devices. Due to the high correlation between emotion and involuntary physiological changes, physiological signals are a prime candidate for emotion analysis. However, due to the need of a huge amount of training data for a high-quality machine learning model, computational complexity becomes a major bottleneck. To overcome this issue, brain-inspired hyperdimensional (HD) computing, an energy-efficient and fast learning computational paradigm, has a high potential to achieve a balance between accuracy and the amount of necessary training data. We propose an HD Computingbased Multimodality Emotion Recognition (HDC-MER). HDC-MER maps real-valued features to binary HD vectors using a random nonlinear function, and further encodes them over time, and fuses across different modalities including GSR, ECG, and EEG. The experimental results show that, compared to the best method using the full training data, HDC-MER achieves higher classification accuracy for both valence (83.2% vs. 80.1%) and arousal (70.1% vs. 68.4%) using only 1/4 training data. HDC-MER also achieves at least 5% higher averaged accuracy compared to all the other methods in any point along the learning curve
Statistical analysis for longitudinal MR imaging of dementia
Serial Magnetic Resonance (MR) Imaging can reveal structural atrophy in the brains of
subjects with neurodegenerative diseases such as Alzheimer’s Disease (AD). Methods of
computational neuroanatomy allow the detection of statistically significant patterns of
brain change over time and/or over multiple subjects. The focus of this thesis is the
development and application of statistical and supporting methodology for the analysis
of three-dimensional brain imaging data. There is a particular emphasis on longitudinal
data, though much of the statistical methodology is more general.
New methods of voxel-based morphometry (VBM) are developed for serial MR data,
employing combinations of tissue segmentation and longitudinal non-rigid registration.
The methods are evaluated using novel quantitative metrics based on simulated data.
Contributions to general aspects of VBM are also made, and include a publication concerning
guidelines for reporting VBM studies, and another examining an issue in the
selection of which voxels to include in the statistical analysis mask for VBM of atrophic
conditions.
Research is carried out into the statistical theory of permutation testing for application
to multivariate general linear models, and is then used to build software for the analysis
of multivariate deformation- and tensor-based morphometry data, efficiently correcting
for the multiple comparison problem inherent in voxel-wise analysis of images. Monte
Carlo simulation studies extend results available in the literature regarding the different
strategies available for permutation testing in the presence of confounds.
Theoretical aspects of longitudinal deformation- and tensor-based morphometry are
explored, such as the options for combining within- and between-subject deformation
fields. Practical investigation of several different methods and variants is performed for a
longitudinal AD study