我的台灣, 看見心靈的故鄉 =2009林磐聳藝術與設計展

The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

Deep Learning for EEG-Based Preference Classification in Neuromarketing

A review of the smart world

Automatic modulation classification of unknown wireless signals has been studied as a key technology in several cognitive radio and military applications. However, few studies have examined the performance of cooperative classification, in which spatially scattered nodes share their data to generate an overall decision of the unknown modulation scheme. In this paper, we propose a centralized feature-level cooperative classification framework using maximum likelihood (ML) combining algorithm. Each node estimates a set of features, based on high-order cumulants, that are combined at a fusion center using ML to produce a final decision. We present new theoretical models for ML combining performance, as well as computer simulations, under both additive white Gaussian noise and flat Rayleigh fading channels. In addition, a new hierarchical framework for cooperative modulation classification of larger sets of modulation schemes is presented. Finally, the performance of the cooperative framework with correlated signals is studied. Analysis and simulations validate the theoretical models and prove the efficiency of the proposed cooperative framework under different scenarios.

Cooperative Cumulants-Based Modulation Classification in Distributed Networks

We seek to align agent behavior with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user knows the rewards and unsafe states, but querying the user is expensive. To address this challenge, we propose an algorithm that safely and interactively learns a model of the user's reward function. We start with a generative model of initial states and a forward dynamics model trained on off-policy data. Our method uses these models to synthesize hypothetical behaviors, asks the user to label the behaviors with rewards, and trains a neural network to predict the rewards. The key idea is to actively synthesize the hypothetical behaviors from scratch by maximizing tractable proxies for the value of information, without interacting with the environment. We call this method reward query synthesis via trajectory optimization (ReQueST). We evaluate ReQueST with simulated users on a state-based 2D navigation task and the image-based Car Racing video game. The results show that ReQueST significantly outperforms prior methods in learning reward models that transfer to new environments with different initial state distributions. Moreover, ReQueST safely trains the reward model to detect unsafe states, and corrects reward hacking before deploying the agent.

Learning Human Objectives by Evaluating Hypothetical Behavior

Eye-blinks are known to substantially contaminate EEG signals, and thereby severely impact the decoding of EEG signals in various medical and scientific applications. In this work, we consider the problem of eye-blink detection that can then be employed to reliably remove eye-blinks from EEG signals. We propose a fully automated and unsupervised eyeblink detection algorithm, Blink that self-learns user-specific brainwave profiles for eye-blinks. Hence, Blink does away with any user training or manual inspection requirements. Blink functions on a single channel EEG, and is capable of estimating the start and end timestamps of eye-blinks in a precise manner. We collect four different eye-blink datasets and annotate 2300+ eye-blinks to evaluate the robustness performance of Blink across headsets (OpenBCI and Muse), eye-blink types (voluntary and involuntary), and various user activities (watching a video, reading an article, and attending to an external stimulation). The Blink algorithm performs consistently with an accuracy of over 98% for all the tasks with an average precision of 0.934. The source code and annotated datasets are released publicly for reproducibility and further research. To the best of our knowledge, this is the first ever annotated eye-blink EEG dataset released in the public domain.

Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals

In this work, we consider the problem of detection and interpretation of user preferences using their brainwaves. The specific goal in this context is to determine the preference ranking for a set of objects by solely relying on the brain activity of a user who is wearing an EEG headset wearable. We first establish the feasibility of object ranking (based on an EEG wearable) by a trial and error based analysis of the EEG signals. We then present a machine learning algorithm Cerebro, which can learn the specific nuances of the user's brainwaves for preferences to accurately rank the objects. We measure the accuracy of the algorithm in terms of the Normalized Discounted Cumulative Gain (NDCG), and show that it performs well when trained on 7 objects, and evaluated on 3 objects for the 14 users.

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Cerebro: A Wearable Solution to Detect and Track User Preferences using Brainwaves

Accelerating Reinforcement Learning using EEG-based implicit human feedback

Providing Reinforcement Learning (RL) agents with human feedback can dramatically improve various aspects of learning However, previous methods require human observer to give inputs explicitly (eg, press buttons, voice interface), burdening the human in the loop of RL agent's learning process Further, it is sometimes difficult or impossible to obtain the explicit human advise (feedback), eg, autonomous driving, disabled rehabilitation, etc In this work, we investigate capturing human's intrinsic reactions as implicit (and natural) feedback through EEG in the form of error-related potentials (ErrP), providing a natural and direct way for humans to improve the RL agent learning As such, the human intelligence can be integrated via implicit feedback with RL algorithms to accelerate the learning of RL agent We develop three reasonably complex 2D discrete navigational games to experimentally evaluate the overall performance of the proposed work Major contributions of our work are as follows, 
(i) we propose and experimentally validate the zero-shot learning of ErrPs, where the ErrPs can be learned for one game, and transferred to other unseen games, (ii) we propose a novel RL framework for integrating implicit human feedbacks via ErrPs with RL agent, improving the label efficiency and robustness to human mistakes, and (iii) compared to prior works, we scale the application of ErrPs to reasonably complex environments, and demonstrate the significance of our approach for accelerated learning through real user experiments

Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback

Biometric authentication is becoming popular in a varied range of applications because of its unique specificity for an individual user. In this context, electro-encephalogram (EEG) signals from a user is an interesting candidate for authentication. In this work, we specifically focus on the EEG signal corresponding to the human eye-blink to create an authentication system that could be used to distinguish between multiple users accurately and efficiently while also being burden-less and convenient to the users. We use a dataset of eye-blink related EEG signals, collected from 20 users, to study our solution. Our results show that blink signals can be used for accurately distinguishing between different users and hence can be used for authentication.

Mohit Agarwal

Papers

Blink: A Fully Automated Unsupervised Algorithm for Eye-Blink Detection in EEG Signals

Cerebro: A Wearable Solution to Detect and Track User Preferences using Brainwaves

Accelerating Reinforcement Learning using EEG-based implicit human feedback

Accelerating Reinforcement Learning Agent with EEG-based Implicit Human Feedback

Blink to Get In: Biometric Authentication for Mobile Devices using EEG Signals