There is a growing body of evidence that important aspects of human cognition have been marginalized, or overlooked, by traditional cognitive science. In particular, the use of laboratory-based experiments in which stimuli are artificial, and response options are fixed, inevitably results in findings that are less ecologically valid in relation to real-world behavior. In the present review we highlight the opportunities provided by a range of new mobile technologies that allow traditionally lab-bound measurements to now be collected during natural interactions with the world. We begin by outlining the theoretical support that mobile approaches receive from the development of embodied accounts of cognition, and we review the widening evidence that illustrates the importance of examining cognitive processes in their context. As we acknowledge, in practice, the development of mobile approaches brings with it fresh challenges, and will undoubtedly require innovation in paradigm design and analysis. If successful, however, the mobile cognition approach will offer novel insights in a range of areas, including understanding the cognitive processes underlying navigation through space and the role of attention during natural behavior. We argue that the development of real-world mobile cognition offers both increased ecological validity, and the opportunity to examine the interactions between perception, cognition and action—rather than examining each in isolation.

The distribution of attention between competing processing demands can have dramatic real-world consequences, however little is known about how limited attentional resources are distributed during real-world behaviour. Here we employ mobile EEG to characterise the allocation of attention across multiple sensory-cognitive processing demands during naturalistic movement. We used a neural marker of attention, the Event-Related Potential (ERP) P300 effect, to show that attention to targets is reduced when human participants walk compared to when they stand still. In a second experiment, we show that this reduction in attention is not caused by the act of walking per se. A third experiment identified the independent processing demands driving reduced attention to target stimuli during motion. ERP data reveals that the reduction in attention seen during walking reflects the linear and additive sum of the processing demands produced by visual and inertial stimulation. The mobile cognition approach used here shows how limited resources are precisely re-allocated according to the sensory processing demands that occur during real-world behaviour.

Steady-States Visually Evoked Potentials (SSVEP) refer to the sustained rhythmic activity observed in surface electroencephalography (EEG) in response to the presentation of repetitive visual stimuli (RVS). Due to their robustness and rapid onset, SSVEP have been widely used in Brain Computer Interfaces (BCI). However, typical SSVEP stimuli are straining to the eyes and present risks of triggering epileptic seizures. Reducing visual stimuli contrast or extending their frequency range both appear as relevant solutions to address these issues. It however remains sparsely documented how BCI performance is impacted by these features and to which extent user experience can be improved. We conducted two studies to systematically characterize the effects of frequency and amplitude depth reduction on SSVEP response. The results revealed that although high frequency stimuli improve visual comfort, their classification performance were not competitive enough to design a reliable/responsive BCI. Importantly, we found that the amplitude depth reduction of low frequency RVS is an effective solution to improve user experience while maintaining high classification performance. These findings were further validated by an online T9 SSVEP-BCI in which stimuli with 40% amplitude depth reduction achieved comparable results (>90% accuracy) to full amplitude stimuli while significantly improving user experience.

The study of cognitive processes underlying natural behaviors implies departing from computerized paradigms and artificial experimental probes. The present study aims to assess the feasibility of capturing neural markers (P300 ERPs) of cognitive processes evoked in response to the identification of task-relevant objects embedded in a real-world environment. To this end, EEG and eye-tracking data were recorded while participants attended stimuli presented on a tablet and while they searched for books in a library. Initial analyses of the library data revealed that P300-like features shifted in time. A Dynamic Time Warping analysis confirmed the presence of P300 ERP in the library condition. Library data were then lag-corrected based on cross-correlation coefficients. Together, these approaches uncovered P300 ERP responses in the library recordings. These findings highlight the relevance of scalable experimental designs, joint brain and body recordings, and template-matching analyses to capture cognitive events during natural behaviors.

Steady-States Visually Evoked Potentials (SSVEP) is currently one of the most widely used paradigms in Brain-Computer Interfaces (BCI). Although the SSVEP-BCI are characterized by their high and robust classification performance, the repetitive presentation of flickering stimuli is uncomfortable from a user experience perspective point of view. Indeed, the low-level visual features of SSVEP stimuli make them straining to the eyes over time and could be disruptive to the execution of tasks requiring sustained attention. They could even induce epileptic seizures. This study explores the reduction of the stimulation amplitude depth (a magnitude diminution of 90%) for the design of SSVEP stimuli as a solution to improve user comfort. The classification accuracy obtained by different pipelines was systematically compared between low amplitude and standard full amplitude SSVEP stimuli. The results reveal high classification accuracy for both high (99.8%) and low magnitude (80.2%) stimuli using the Task-Related Component Analysis (TRCA) classification method. The present findings demonstrate the validity of reducing SSVEP stimuli amplitude to increase users' comfort paving the way for transparent BCI operation.