Deep learning has revolutionized the field of computer vision, where convolutional neural networks (CNNs) extract complex patterns of information from large datasets. The use of deep networks in neuroscience is mainly focused to neuroimaging or brain computer interface -BCI- applications. In electroencephalography (EEG) research, multivariate pattern analysis (MVPA) mainly relies on linear algorithms, which require a homogeneous dataset and assume that discriminant features appear at consistent latencies and electrodes across trials. However, neural responses may shift in time or space during an experiment, resulting in under-estimation of discriminant features. Here, we aimed at using CNNs to classify EEG responses to external stimuli, by taking advantage of time- and space- unlocked neural activity, and at examining how discriminant features change over the course of an experiment, on a trial by trial basis. We present a novel pipeline, consisting of data augmentation, CNN training, and feature visualization techniques, fine-tuned for MVPA on EEG data. Our pipeline provides high classification performance and generalizes to new datasets. Additionally, we show that the features identified by the CNN for classification are electrophysiologically interpretable and can be reconstructed at the single-trial level to study trial-by-trial evolution of class-specific discriminant activity. The developed pipeline was compared to commonly used MVPA algorithms like logistic regression and support vector machines, as well as to shallow and deep convolutional neural networks. Our approach yielded significantly higher classification performance than existing MVPA techniques (p = 0.006) and comparable results to other CNNs for EEG data. In summary, we present a novel deep learning pipeline for MVPA of EEG data, that can extract trial-by-trial discriminative activity in a data-driven way.

The field of sleep research is both broad and rapidly evolving. It spans from the diagnosis of sleep-related disorders to investigations of how sleep supports memory consolidation. The study of sleep includes a variety of approaches, starting with the sole focus on the visual interpretation of polysomnography characteristics and extending to the emergent use of advanced signal processing tools. Insights gained using artificial intelligence (AI) are rapidly reshaping the understanding of sleep-related disorders, enabling new approaches to basic neuroscientific studies. In this opinion article, we explore the emergent role of AI in sleep research, along two different axes: one clinical and one fundamental. In clinical research, we emphasize the use of AI for automated sleep scoring, diagnosing sleep-wake disorders and assessing measurements from wearable devices. In fundamental research, we highlight the use of AI to better understand the functional role of sleep in consolidating memories. While AI is likely to facilitate new advances in the field of sleep research, we also address challenges, such as bridging the gap between AI innovation and the clinic and mitigating inherent biases in AI models. AI has already contributed to major advances in the field of sleep research, and mindful deployment has the potential to enable further progress in the understanding of the neuropsychological benefits and functions of sleep.

Electrophysiological studies are profiting from multivariate pattern analysis methods. However, these mostly rely on machine-learning algorithms that assume consistent response latencies across trials and individuals. Deep learning provides high performance without such assumptions, but often at the cost of interpretability of learned features. Here, we evaluated how the interpretability of deep learning for electroencephalography (EEG) data is affected by preprocessing choices, the network’s architecture, and the way the learned features are extracted and visualized. We trained two convolutional neural networks (CNN): (1) ResNet, a residual network, and (2) EEGNet, which leverages spatiotemporal properties of EEG. We trained these networks to decode single-trial EEG responses to three different visual stimuli (visual dataset) and to the presence of a sound (auditory dataset). We then extracted and visualized learned features with two gradient-based techniques: saliency and gradient-weighted activation maps (GradCam). Results showed that EEGNet and ResNet performed at a similar level. Yet, visualization of learned features revealed that different architectures learn different aspects of the data. Between the two CNNs, EEGNet features had a higher similarity to the EEG data than ResNet features. Moreover, the latency and distribution of important electrodes varied depending on the visualization technique. GradCam provided features more similar to EEG data than those with saliency, emphasizing the impact of the feature extraction method on interpretability. Our results call for careful consideration of network architecture and feature visualization methods to improve interpretability, which is a crucial step for advancing the use of deep learning in EEG research.

Foreseeing the future outcomes is the art of decision-making. Substantial evidence shows that, during choice deliberation, the brain can retrieve prospective decision outcomes. However, decisions are seldom made in a vacuum. Context carries information that can radically affect the outcomes of a choice. Nevertheless, most investigations of retrieval processes examined decisions in isolation, disregarding the context in which they occur. Here, we studied how context shapes prospective outcome retrieval during deliberation. We designed a decision-making task where participants were presented with object–context pairs and made decisions which led to a certain outcome. We show during deliberation, likely outcomes were retrieved in transient patterns of neural activity, as early as 3 s before participants decided. The strength of prospective outcome retrieval explains participants’ behavioral efficiency, but only when context affects the decision outcome. Our results suggest context imparts strong constraints on retrieval processes and how neural representations are shaped during decision-making.