10,870 research outputs found
Evaluating Content-centric vs User-centric Ad Affect Recognition
Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation
(ICMI) 201
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel EEG time-series, and demonstrate its advantages in the
context of mental load classification task. First, we transform EEG activities
into a sequence of topology-preserving multi-spectral images, as opposed to
standard EEG analysis techniques that ignore such spatial information. Next, we
train a deep recurrent-convolutional network inspired by state-of-the-art video
classification to learn robust representations from the sequence of images. The
proposed approach is designed to preserve the spatial, spectral, and temporal
structure of EEG which leads to finding features that are less sensitive to
variations and distortions within each dimension. Empirical evaluation on the
cognitive load classification task demonstrated significant improvements in
classification accuracy over current state-of-the-art approaches in this field.Comment: To be published as a conference paper at ICLR 201
Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations
Fully automated decoding of human activities and intentions from direct
neural recordings is a tantalizing challenge in brain-computer interfacing.
Most ongoing efforts have focused on training decoders on specific, stereotyped
tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in
natural settings requires adaptive strategies and scalable algorithms that
require minimal supervision. Here we propose an unsupervised approach to
decoding neural states from human brain recordings acquired in a naturalistic
context. We demonstrate our approach on continuous long-term
electrocorticographic (ECoG) data recorded over many days from the brain
surface of subjects in a hospital room, with simultaneous audio and video
recordings. We first discovered clusters in high-dimensional ECoG recordings
and then annotated coherent clusters using speech and movement labels extracted
automatically from audio and video recordings. To our knowledge, this
represents the first time techniques from computer vision and speech processing
have been used for natural ECoG decoding. Our results show that our
unsupervised approach can discover distinct behaviors from ECoG data, including
moving, speaking and resting. We verify the accuracy of our approach by
comparing to manual annotations. Projecting the discovered cluster centers back
onto the brain, this technique opens the door to automated functional brain
mapping in natural settings
On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing
Objective: While Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing.
Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli.
Results: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli.
Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients
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