Towards EEG-based BCI driven by emotions for addressing BCI-Illiteracy: a meta-analytic review

Abstract

Many critical aspects affect the correct operation of a Brain Computer Interface. The term "BCIilliteracy" describes the impossibility of using a BCI paradigm. At present, a universal solution does not exist and seeking innovative protocols to drive a BCI is mandatory. This work presents a meta-analytic review on recent advances in emotions recognition with the perspective of using emotions as voluntary, stimulus-independent, commands for BCIs. 60 papers, based on electroencephalography measurements, were selected to evaluate what emotions have been most recognized and what brain regions were activated by them. It was found that happiness, sadness, anger and calm were the most recognized emotions. Most discriminant locations for emotions recognition and for the particular case of discrete emotions recognition were identified in the temporal, frontal and parietal areas. The meta-analysis was mainly performed on stimulus-elicited emotions, due to the limited amount of literature about self-induced emotions. The obtained results represent a good starting point for the development of BCI driven by emotions and allow to: 1) ascertain that emotions are measurable and recognizable one from another 2) select a subset of most recognizable emotions and the corresponding active brain regions.

Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives

“…Consumer-grade devices, although not accurate enough for neuroscience research and critical control tasks, have been reported to be a feasible choice for applications such as affective computing (Duvinage et al, 2013;Nijboer et al, 2015;Maskeliunas et al, 2016). ER by EEG has been widely explored in the literature (Alarcao and Fonseca, 2017;Spezialetti et al, 2018). Most commonly used features can be roughly classified by the domain from which they are extracted (time, frequency, time-frequency).…”

“…ER by EEG has been widely explored in the literature (Alarcao and Fonseca, 2017 ; Spezialetti et al, 2018 ). Most commonly used features can be roughly classified by the domain from which they are extracted (time, frequency, time–frequency).…”

Personalized models for facial emotion recognition through transfer learning

“…Affective computing researches aim to furnish computers with emotional intelligence [51] to allow them to be genuinely intelligent and support natural human-machine interaction (HMI). Emotion recognition has several applications in different areas such as marketing [18], safe and autonomous driving [22], mental health monitoring [17], brain-computer interfaces [65], social security [75], robotics [55].…”

Control Proporcional Multiresolución para un Robot de 2GDL a través de un Sistema BCI

“…Una Interfaz Cerebro computador (BCI) usa señales del cerebro que proporcionan un método directo de comunicación entre una computadora y otros dispositivos, una BCI puede ayudar a reestablecer la comunicación con personas que sufrieron un trastorno de movimiento (Thompsona, 2017). Una BCI proporciona herramientas de control hacia un entorno externo como una alternativa a las vías tradicionales como son músculos y nervios y son basados en la monitorización directa de la actividad cerebral (M. Spezialetti, 2018).…”