'Girlfriends and Strawberry Jam’: Tagging Memories, Experiences, and Events for Future Retrieval

In this short paper we have some preliminary thoughts about tagging everyday life events in order to allow future retrieval of events or experiences related to events. Elaboration of these thoughts will be done in the context of the recently started Network of Excellence PetaMedia (Peer-to-Peer Tagged Media) and the Network of Excellence SSPNet (Social Signal Processing), to start in 2009, both funded by the European Commission's Seventh Framework Programme. Descriptions of these networks will be given later in this paper

Pushing the boundaries of EEG-based emotion classification using consumer-grade wearable brain-computer interfacing devices and ensemble classifiers

Emotion classification using features derived from electroencephalography (EEG) is currently one of the major research areas in big data. Although this area of research is not new, the current challenge is now to move from medical-grade EEG acquisition devices to consumer-grade EEG devices. The overwhelmingly large majority of reported studies that have achieved high success rates in such research uses equipment that is beyond the reach of the everyday consumer. Subsequently, EEG-based emotion classification applications, though highly promising and worthwhile to research, largely remain as academic research and not as deployable solutions. In this study, we attempt to use consumer-grade EEG devices commonly referred to as wearable EEG devices that are very economical in cost but have a limited number of sensor electrodes as well as limited signal resolution. Hence, this greatly reduces the number and quality of available EEG signals that can be used as classification features. Additionally, we also attempt to classify into 4 distinct classes as opposed to the more common 2 or 3 class emotion classification task. Moreover, we also additionally attempt to conduct inter-subject classification rather than just intra-subject classification, which again the former is much more challenging than the latter. Using a test cohort of 31 users with stimuli presented via an immersive virtual reality environment, we present results that show that classification accuracies were able to be pushed to beyond 85% using ensemble classification methods in the form of Random Forest

Recent Advances in Neural Recording Microsystems

The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field

Smart Brain Interaction Systems for Office Access and Control in Smart City Context

Over the past decade, the term “smart cities” has been worldwide priority for city planning by governments. Planning smart cities implies identifying key drivers for transforming into more convenient, comfortable, and safer life. This requires equipping the cities with appropriate smart technologies and infrastructure. Smart infrastructure is a key component in planning smart cities: smart places, transportation, health and education systems. Smart offices present the concept of workplaces that respond to user’s needs and allow less commitment to routine tasks. Smart offices solutions enable employees to change status of the surrounding environment upon the change of user’s preferences using the changes in the user’s biometrics measures. Meanwhile, smart office access and control through brain signals is quite recent concept. Hence, smart offices provide access and services availability at each moment using smart personal identification (PI) interfaces that responds only to the personal thoughts/preferences issued by the office employee not any other person. Hence, authentication and control systems could benefit from the biometrics. Yet these systems are facing efficiency and accessibility challenges in terms of unimodality. This chapter addresses those problems and proposes a prototype for multimodal biometric person identification control system for smart office access and control as a solution

Analyzing the Scalability of Parallel Microwire Arrays for Neural Recording

Brain-computer interfaces (BCI) improve the quality of life for patients with severe motor disabilities and sensory impairment by providing them a direct way to communicate with the outside world through computers. To gain higher temporal resolution for better devices, intracortical neural electrodes, such as microwire arrays, are used. Microwire electrode arrays bonded to CMOS sensors, for intracortical neural recordings, have been claimed to be scalable. Microwire electrode arrays of varying diameters and densities were constructed and evaluated for percentage connectivity after interfacing with a custom-made CMOS sensor. The results demonstrate that there is no significant difference in the mean connectivity between a 3 mm and a 12 mm bundle as well as between arrays that have a wire-to-wire distance of 200 μm versus 100 μm, confirming the scalability of microwire electrode arrays. Understanding array scalability allows for better electrodes to be built for higher resolution neural recordings, which can help those who suffer from motor or sensory disabilities regain a better quality of life by re-establishing some independence

Breaking fresh ground in human–media interaction research

Human-Media Interaction research is devoted to methods and situations where humans individually or collectively interact with digital media, systems, devices and environments. Novel forms of interaction paradigms have been enabled by new sensor and actuator technology in the last decades, combining with advances in our knowledge of human-human interaction and human behavior in general when designing user interfaces