Emotional Brain-Computer Interfaces

Research in Brain-computer interface (BCI) has significantly increased during the last few years. In addition to their initial role as assisting devices for the physically challenged, BCIs are now proposed for a wider range of applications. As in any HCI application, BCIs can also benefit from adapting their operation to the emotional state of the user. BCIs have the advantage of having access to brain activity which can provide signicant insight into the user's emotional state. This information can be utilized in two manners. 1) Knowledge of the inuence of the emotional state on brain activity patterns can allow the BCI to adapt its recognition algorithms, so that the intention of the user is still correctly interpreted in spite of signal deviations induced by the subject's emotional state. 2) The ability to recognize emotions can be used in BCIs to provide the user with more natural ways of controlling the BCI through affective modulation. Thus, controlling a BCI by recollecting a pleasant memory can be possible and can potentially lead to higher information transfer rates.\ud These two approaches of emotion utilization in BCI are elaborated in detail in this paper in the framework of noninvasive EEG based BCIs

On Error-related Potentials during Sensorimotor-based Brain-Computer Interface: Explorations with a Pseudo-Online Brain-Controlled Speller

Objective: Brain-computer interface (BCI) spelling is a promising communication solution for people in paralysis. Currently, BCIs suffer from imperfect decoding accuracy which calls for methods to handle spelling mistakes. Detecting error-related potentials (ErrPs) has been early identified as a potential remedy. Nevertheless, few works have studied the elicitation of ErrPs during engagement with other BCI tasks, especially when BCI feedback is provided continuously. Here, we test the possibility of correcting errors during pseudo-online Motor Imagery (MI) BCI spelling through ErrPs, and investigate whether BCI feedback hinders their generation. Results: Ten subjects performed a series of MI spelling tasks with and without observing BCI feedback. The average pseudo-online ErrP detection accuracy was found to be significantly above the chance level in both conditions and did not significantly differ between the two (74% with, and 78% without feedback). Conclusions: Our results support the possibility to detect ErrPs during MI-BCI spelling and suggest the absence of any BCI feedback-related interference

EEG-Based Brain-Computer Interfacing via Motor-Imagery: Practical Implementation and Feature Analysis

The human brain is the most intriguing and complex signal processing unit ever known to us. A unique characteristic of our brain is its plasticity property, i.e., the ability of neurons to modify their behavior (structure and functionality) in response to environmental diversity. The plasticity property of brain has motivated design of brain-computer interfaces (BCI) to develop an alternative form of communication channel between brain signals and the external world. The BCI systems have several therapeutic applications of significant importance including but not limited to rehabilitation/ assistive systems, rehabilitation robotics, and neuro-prosthesis control. Despite recent advancements in BCIs, such systems are still far from being reliably incorporated within humanmachine inference networks. In this regard, the thesis focuses on Motor Imagery (MI)-based BCI systems with the objective of tackling some key challenges observed in existing solutions. The MI is defined as a cognitive process in which a person imagines performing a movement without peripheral (muscle) activation. At one hand, the thesis focuses on feature extraction, which is one of the most crucial steps for the development of an effective BCI system. In this regard, the thesis proposes a subject-specific filtering framework, referred to as the regularized double-band Bayesian (R-B2B) spectral filtering. The proposed R-B2B framework couples three main feature extraction categories, namely filter-bank solutions, regularized techniques, and optimized Bayesian mechanisms to enhance the overall classification accuracy of the BCI. To further evaluate the effects of deploying optimized subject-specific spectra-spatial filters, it is vital to examine and investigate different aspects of data collection and in particular, effects of the stimuli provided to subjects to trigger MI tasks. The second main initiative of the thesis is to propose an element of experimental design dealing with MI-based BCI systems. In this regard, we have implemented an EEG-based BCI system and constructed a benchmark dataset associated with 10 healthy subjects performing actual movement and MI tasks. To investigate effects of stimulus on the overall achievable performance, four different protocols are designed and implemented via introduction of visual and voice stimuli. Finally, the work investigates effects of adaptive trimming of EEG epochs resulting in an adaptive and subject-specific solution

Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study

A corrigendum on Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study by Ibáñez, J., Monge-Pereira, E., Molina-Rueda, F., Serrano, J. I., del Castillo, M. D., Cuesta-Gómez, A., et al. (2017). Front. Neurosci. 11:126. doi: 10.3389/fnins.2017.00126. In the recently published article, there were incorrect and missing contents in the Acknowledgments section

User-Centred BCI Videogame Design

International audienceThis chapter aims to offer a user-centred methodological framework to guide the design and evaluation of Brain-Computer Interface videogames. This framework is based on the contributions of ergonomics to ensure these games are well suited for their users (i.e., players). It provides methods, criteria and metrics to complete the different phases required by ae human-centred design process. This aims to understand the context of use, specify the user needs and evaluate the solutions in order to define design choices. Several ergonomic methods (e.g., interviews, longitudinal studies, user based testing), objective metrics (e.g., task success, number of errors) and subjective metrics (e.g., mark assigned to an item) are suggested to define and measure the usefulness, usability, acceptability, hedonic qualities, appealingness, emotions related to user experience, immersion and presence to be respected. The benefits and contributions of the user centred framework for the ergonomic design of these Brain-Computer Interface Videogames are discussed

Cortical motor prosthetics: the development and use for paralysis

The emerging research field of Brain Computer Interfaces (BCIs) has created an invasive type of BCI, the Cortical Motor Prosthetic (CMP) or invasive BCI (iBCI). The goal is to restore lost motor function via prosthetic control signals to individuals who have long-term paralysis. The development of the CMP consists of two major entities: the implantable, chronic microelectrode array (MEA) and the data acquisition hardware (DAQ) specifically the decoder. The iBCI's function is to record primary motor cortex (M1) neural signals via chronic MEA and translate into a motor command via decoder extraction algorithms that can control a prosthetic to perform the intended movement. The ultimate goal is to use the iBCI as a clinical tool for individuals with long-term paralysis to regain lost motor functioning. Thus, the iBCI is a beacon of hope that could enable individuals to independently perform daily activities and interact once again with their environment. This review seeks to accomplish two major goals. First, elaborate upon the development of the iBCI and focus on the advancements and efforts to create a viable system. Second, illustrate the exciting improvements in the iBCI's use for reaching and grasping actions and in human clinical trials. The ultimate goal is to use the iBCI as a clinical tool for individuals with long-term paralysis to regain movement control. Despite the promise in the iBCI, many challenges, which are described in this review, persist and must be overcome before the iBCI can be a viable tool for individuals with long-term. iBCI future endeavors aim to overcome the challenges and develop an efficient system enhancing the lives of many living with paralysis. Standard terms: Intracortical Brain Computer Interface (iBCI), Intracortical Brain Machine Interface (iBMI), Cortical Motor Prosthetic (CMP), Neuromotor Prostheses (NMP), Intracortical Neural Prosthetics, Invasive Neural Prosthetic all terms used interchangeabl