Dorsolateral prefrontal cortex-based control with an implanted brain–computer interface

Leinders, Sacha; Vansteensel, Mariska J.; Branco, Mariana P.; Freudenburg, Zac V.; Pels, Elmar; Vijgh, Benny van der; Zandvoort, M.J.E. van; Ramsey, N.F.; Aarnoutse, Erik J.

doi:10.1038/s41598-020-71774-5

Cited by 11 publications

(9 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because the muscles of paralyzed patients undergo disuse atrophy [ 172 ], the replacement of motor function is usually achieved through control of robotic devices. Surgically implanted BCIs detect electric signals from the cortical surface using electrocorticography (ECoG), which ensures high spatial resolution [ 173 ]. For reliable control of external robotic devices, however, the electric activity should be recorded from regions of the brain cortex where voluntary mental operations could elicit certain discernible wavefronts, such as sensorimotor rhythms (SMRs) or the so-called P300 evoked response.…”

Section: Virtual Reality For Replacement Of Functionmentioning

confidence: 99%

Virtual Reality for Neurorehabilitation and Cognitive Enhancement

et al. 2021

View full text Add to dashboard Cite

Our access to computer-generated worlds changes the way we feel, how we think, and how we solve problems. In this review, we explore the utility of different types of virtual reality, immersive or non-immersive, for providing controllable, safe environments that enable individual training, neurorehabilitation, or even replacement of lost functions. The neurobiological effects of virtual reality on neuronal plasticity have been shown to result in increased cortical gray matter volumes, higher concentration of electroencephalographic beta-waves, and enhanced cognitive performance. Clinical application of virtual reality is aided by innovative brain–computer interfaces, which allow direct tapping into the electric activity generated by different brain cortical areas for precise voluntary control of connected robotic devices. Virtual reality is also valuable to healthy individuals as a narrative medium for redesigning their individual stories in an integrative process of self-improvement and personal development. Future upgrades of virtual reality-based technologies promise to help humans transcend the limitations of their biological bodies and augment their capacity to mold physical reality to better meet the needs of a globalized world.

show abstract

Section: Virtual Reality For Replacement Of Functionmentioning

confidence: 99%

Virtual Reality for Neurorehabilitation and Cognitive Enhancement

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In a similar context, the animals with an electrode on the parietal cortex in our study also succeeded in BMI learning after a few sessions. In the case of BMI in which signals from the frontal cortex are used, there have been a number of studies on implementing the BMI system using brain signals obtained from the frontal cortex [27][28][29]. Leinders et al showed that BMI training is possible in humans using dorsal lateral prefrontal cortex (DLPFC) ECoG signals [27].…”

Section: Discussionmentioning

confidence: 99%

“…In the case of BMI in which signals from the frontal cortex are used, there have been a number of studies on implementing the BMI system using brain signals obtained from the frontal cortex [27][28][29]. Leinders et al showed that BMI training is possible in humans using dorsal lateral prefrontal cortex (DLPFC) ECoG signals [27]. Widge and Moritz also implemented a closed-loop limbic neurostimulator controlled through the BMI system using spike signals measured in the prefrontal cortex of rodents [29].…”

Section: Discussionmentioning

confidence: 99%

The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance

Jung

Lim

Kwak

et al. 2021

Sensors

View full text Add to dashboard Cite

Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.

show abstract

“…It has been shown that the dLPFC can be a target area for BCI control using mental calculation [15] and this provides an alternative UNP control strategy [17]. In this work, the BCI feedback is generated from the M1 electrodes, but signals are also being simultaneously measured from the dLPFC.…”

Section: The Unp Brain Signalmentioning

confidence: 99%

“…In addition to being used to derive the UNP control signal, frequency power features are commonly used for M1-based EEG [6] and ECoG [16] BCI control and have also been used in dLPFC BCI control [15,17]. For these reasons, we also performed a frequency decomposition on the unsmoothed potential signals to investigate errorrelated signals in the frequency domain.…”

Section: Analysis Of the Error Responsesmentioning

confidence: 99%

The dorsolateral pre-frontal cortex bi-polar error-related potential in a locked-in patient implanted with a daily use brain–computer interface

Freudenburg

Kohneshin

Aarnoutse

et al. 2021

Control Theory Technol.

Self Cite

View full text Add to dashboard Cite

While brain computer interfaces (BCIs) offer the potential of allowing those suffering from loss of muscle control to once again fully engage with their environment by bypassing the affected motor system and decoding user intentions directly from brain activity, they are prone to errors. One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions. Error-related potentials (ErrPs) are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions. Here, we investigate the brain signals of an implanted BCI user suffering from locked-in syndrome (LIS) due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate, for the presence of error-related signals. We first establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex (dLPFC) in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate. Then, we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task. This work represents a first step toward detecting ErrPs during the daily home use of a communications BCI.

show abstract

Dorsolateral prefrontal cortex-based control with an implanted brain–computer interface

Cited by 11 publications

References 26 publications

Virtual Reality for Neurorehabilitation and Cognitive Enhancement

Virtual Reality for Neurorehabilitation and Cognitive Enhancement

The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance

The dorsolateral pre-frontal cortex bi-polar error-related potential in a locked-in patient implanted with a daily use brain–computer interface

Contact Info

Product

Resources

About