Complex memories induced by intracranial electrical brain stimulation are related to complex networks.

The precise and fleeting moment of rich recollection triggered by an environmental cue is difficult to reproduce in the lab. However, epilepsy patients can experience sudden reminiscences after intracranial electrical brain stimulation (EBS). In these cases, the transient brain state related to the activation of the engram and its conscious perception can be recorded using intracerebral EEG (iEEG).

Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: A wavelet-based CNN detector.

Background: Fast-ripples (FR) are short (∼10 ms) high-frequency oscillations (HFO) between 200 and 600 Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for intracerebral EEG (iEEG) recorded from both usual clinical macro-contacts (millimeter scale) and microwires (micrometer scale).

New Method: Step 1 of the detection method is based on a convolutional neural network (CNN) trained using a large database of > 11,000 FR recorded from the iEEG of 38 patients with epilepsy from both macro-contacts and microwires.

Intracranial electrical brain stimulation as an approach to studying the (dis)continuum of memory experiential phenomena.

Déjà vu and involuntary autobiographical memories (IAM) can be induced by intracranial electric brain stimulation in epileptic patients, sometimes in the same individual. We suggest that there may be different types of IAM which should be taken into account and provide several ideas to test the hypothesis of a continuity between IAM and déjà vu phenomena.

Large-scale network dynamics underlying the first few hundred milliseconds after stimulus presentation: An investigation of visual recognition memory using iEEG.

Recognition memory is the ability to recognize previously encountered objects. Even this relatively simple, yet extremely fast, ability requires the coordinated activity of large-scale brain networks. However, little is known about the sub-second dynamics of these networks.