It remains challenging to relate EEG and MEG to underlying circuit processes and comparable experiments on both spatial scales are rare. To close this gap between invasive and non-invasive electrophysiology we developed and recorded human-comparable EEG in macaque monkeys during visual stimulation with colored dynamic random dot patterns. Furthermore, we performed simultaneous microelectrode recordings from 6 areas of macaque cortex and human MEG. Motion direction and color information were accessible in all signals. Tuning of the non-invasive signals was similar to V4 and IT, but not to dorsal and frontal areas. Thus, MEG and EEG were dominated by early visual and ventral stream sources. Source level analysis revealed corresponding information and latency gradients across cortex. We show how information-based methods and monkey EEG can identify analogous properties of visual processing in signals spanning spatial scales from single units to MEG – a valuable framework for relating human and animal studies.

It remains challenging to relate EEG and MEG to underlying circuit processes and comparable experiments on both spatial scales are rare. To close this gap between invasive and noninvasive electrophysiology we developed and recorded human-comparable EEG in macaque monkeys during visual stimulation with colored dynamic random dot patterns. Furthermore, we performed simultaneous microelectrode recordings from 6 areas of macaque cortex and human MEG. Motion direction and color information were accessible in all signal types. Tuning of the non-invasive signals was similar to V4 and IT, but not to dorsal and frontal areas. Thus, MEG and EEG were dominated by early visual and ventral stream sources. Source level analysis revealed corresponding information and latency gradients across the cortex. We show how information-based methods and monkey EEG can identify analogous properties of visual processing in signals spanning spatial scales from single units to MEG -a valuable framework for relating human and animal studies.

Humans can make abstract choices independent of motor actions. However, little is known about the functional role and neural representation of abstract choices. Here, we show that in the human brain choices are represented in an abstract, motor-independent manner, even when they are directly linked to an action. To disentangle sensory, choice, and motor aspects of decision-making, we measured MEG signals while human participants made choices with known and unknown motor response mapping. Using multivariate decoding, we found stimulus, choice and response information with distinct cortical distributions. Choice representations were invariant to whether or not the response mapping was known during stimulus presentation. Furthermore, neuronal choice representations predicted decision confidence and occupied distinct representational spaces from both stimulus and motor signals. Our results uncover abstract neuronal choice signals that generalize to embodied decisions. This suggests a general role of an abstract stage in human decision-making.

Speech, as the spoken form of language, is fundamental for human communication. The phenomenon of covert inner speech implies a functional independence of speech content and motor production. However, it remains unclear how a flexible mapping between speech content and production is achieved on the neural level. To address this, we recorded magnetoencephalography (MEG) in humans performing a rule-based vocalization task. On each trial, vocalization content (one of two vowels) and production form (overt or covert) were instructed independently. Using multivariate pattern analysis, we found robust neural information about vocalization content and production, mostly originating from speech areas of the left hemisphere. Production signals dynamically transformed upon presentation of the content cue, whereas content signals remained largely stable throughout the trial. In sum, our results show dissociable neural representations of vocalization content and production in the human brain and provide new insights into the neural dynamics underlying human vocalization.