How humans solve the cocktail party problem remains unknown. However, progress has been made recently thanks to the realization that cortical activity tracks the amplitude envelope of speech. This has led to the development of regression methods for studying the neurophysiology of continuous speech. One such method, known as stimulus-reconstruction, has been successfully utilized with cortical surface recordings and magnetoencephalography (MEG). However, the former is invasive and gives a relatively restricted view of processing along the auditory hierarchy, whereas the latter is expensive and rare. Thus it would be extremely useful for research in many populations if stimulus-reconstruction was effective using electroencephalography (EEG), a widely available and inexpensive technology. Here we show that single-trial (≈60 s) unaveraged EEG data can be decoded to determine attentional selection in a naturalistic multispeaker environment. Furthermore, we show a significant correlation between our EEG-based measure of attention and performance on a high-level attention task. In addition, by attempting to decode attention at individual latencies, we identify neural processing at ∼200 ms as being critical for solving the cocktail party problem. These findings open up new avenues for studying the ongoing dynamics of cognition using EEG and for developing effective and natural brain-computer interfaces.

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

The human ability to understand speech is underpinned by a hierarchical auditory system whose successive stages process increasingly complex attributes of the acoustic input. It has been suggested that to produce categorical speech perception, this system must elicit consistent neural responses to speech tokens (e.g., phonemes) despite variations in their acoustics. Here, using electroencephalography (EEG), we provide evidence for this categorical phoneme-level speech processing by showing that the relationship between continuous speech and neural activity is best described when that speech is represented using both low-level spectrotemporal information and categorical labeling of phonetic features. Furthermore, the mapping between phonemes and EEG becomes more discriminative for phonetic features at longer latencies, in line with what one might expect from a hierarchical system. Importantly, these effects are not seen for time-reversed speech. These findings may form the basis for future research on natural language processing in specific cohorts of interest and for broader insights into how brains transform acoustic input into meaning.

Human electrophysiological (EEG) studies have demonstrated the involvement of alpha band (8- to 14-Hz) oscillations in the anticipatory biasing of attention. In the context of visual spatial attention within bilateral stimulus arrays, alpha has exhibited greater amplitude over parietooccipital cortex contralateral to the hemifield required to be ignored, relative to that measured when the same hemifield is to be attended. Whether this differential effect arises solely from alpha desynchronization (decreases) over the "attending" hemisphere, from synchronization (increases) over the "ignoring" hemisphere, or both, has not been fully resolved. This is because of the confounding effect of externally evoked desynchronization that occurs involuntarily in response to visual cues. Here, bilateral flickering stimuli were presented simultaneously and continuously over entire trial blocks, such that externally evoked alpha desynchronization is equated in precue baseline and postcue intervals. Equivalent random letter sequences were superimposed on the left and right flicker stimuli. Subjects were required to count the presentations of the target letter "X" at the cued hemifield over an 8-s period and ignore the sequence in the opposite hemifield. The data showed significant increases in alpha power over the ignoring hemisphere relative to the precue baseline, observable for both cue directions. A strong attentional bias necessitated by the subjective difficulty in gating the distracting letter sequence is reflected in a large effect size of 2.1 (eta2 = 0.82), measured from the attention x hemisphere interaction. This strongly suggests that alpha synchronization reflects an active attentional suppression mechanism, rather than a passive one reflecting "idling" circuits.