Brain computer interface to enhance episodic memory in human participants

Abstract

Recent research has revealed that neural oscillations in the theta (4–8 Hz) and alpha (9–14 Hz) bands are predictive of future success in memory encoding. Because these signals occur before the presentation of an upcoming stimulus, they are considered stimulus-independent in that they correlate with enhanced memory encoding independent of the item being encoded. Thus, such stimulus-independent activity has important implications for the neural mechanisms underlying episodic memory as well as the development of cognitive neural prosthetics. Here, we developed a brain computer interface (BCI) to test the ability of such pre-stimulus activity to modulate subsequent memory encoding. We recorded intracranial electroencephalography (iEEG) in neurosurgical patients as they performed a free recall memory task, and detected iEEG theta and alpha oscillations that correlated with optimal memory encoding. We then used these detected oscillatory changes to trigger the presentation of items in the free recall task. We found that item presentation contingent upon the presence of pre-stimulus theta and alpha oscillations modulated memory performance in more sessions than expected by chance. Our results suggest that an electrophysiological signal may be causally linked to a specific behavioral condition, and contingent stimulus presentation has the potential to modulate human memory encoding.

Expectation modulates neural representations of valence throughout the human brain

“…In most brain regions sampled, we observed strongly heterogeneous responses, with a mixture of recording sites exhibiting relative HFA increases following rewards and other recording sites exhibiting relative HFA increases following penalties. Because HFA is thought to reflect the summed activity of a large population of local neurons (Nir et al, 2007; Ray et al, 2008; Miller, 2010; Burke et al, 2015), our results suggest that neuronal populations in most human brain regions encode outcome valence in a heterogeneous manner (i.e., some neurons show relative increases following rewards, whereas others show relative increases following penalties). Such heterogeneous encoding patterns have been demonstrated in cortical regions by several monkey single-unit studies during reinforcement learning (Matsumoto et al, 2007; Asaad and Eskandar, 2011), and have been suggested as a reason why univariate functional neuroimaging studies may not be able to detect many cognitive signals when averaging activity within brain regions (Wallis and Kennerley, 2011).…”

EEG Error Prediction as a Solution for Combining the Advantages of Retrieval Practice and Errorless Learning

“…In our experiment, we did not find a particular frequency range that was associated with accuracy or error correction; however, in examining the EEG response across the entire range of frequencies, the model could predict the response in a subsequent session. Now that we know that EEG error prediction is possible, we can use this information to design future studies that will more directly test this relationship (see, e.g., Burke et al, 2015; Salari and Rose, 2016). These possibilities include presenting naming trials only when task-appropriate activity is present, as in Salari and Rose (2016).…”

“…Drawing on prior evidence that changes in alpha and theta waves occurring prior to stimulus presentation can predict successful memory encoding and recognition (e.g., Fell et al, 2011; Merkow et al, 2014), Burke et al (2015) designed an experiment in which the participant’s own EEG response was used to trigger stimulus presentation. The authors hypothesized that if words were only presented when the participant was in an optimal brain state for learning, memory encoding and recall would improve.…”

“…Furthermore, these responses may be specific not only to the task but also to the individual. Results from Burke et al (2015) and Salari and Rose (2016) suggest that electrical activity can and does vary across individuals but can still result in similar behavioral outcomes. Research thus far has investigated brain/behavior relationships in unimpaired individuals and patients with epilepsy, but the relationship between EEG spectral features and behavioral performance on language tasks in persons with stroke and aphasia has not yet been established.…”

Interfacing brain with computer to improve communication and rehabilitation after brain damage

“…Compared with the advancements in BCI application for motor recovery after stroke, the literature on BCI systems that target cognitive deficits is limited (Burke et al, 2014). To outline reasonable approaches, BCI researchers should refer to current treatment strategies for neuropsychological deficits and to the wide literature on neurofeedback approaches for cognitive enhancement.…”