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Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

Hiremath SV, Chen W, Wang W, Foldes S, Yang Y, Tyler-Kabara EC, Collinger JL, Boninger ML - Front Integr Neurosci (2015)

Bottom Line: A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively.We call this process BCI learning, and it often requires significant effort and time.Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Veterans Affairs, Human Engineering Research Laboratories Pittsburgh, PA, USA.

ABSTRACT
A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

No MeSH data available.


Artificial mapping for ECoG-based brain control used in Wang et al. (2013). Brain activities corresponding to thumb and elbow movements are mapped on to a two-dimensional workspace to serve as the basis for 2D cursor control.
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Figure 2: Artificial mapping for ECoG-based brain control used in Wang et al. (2013). Brain activities corresponding to thumb and elbow movements are mapped on to a two-dimensional workspace to serve as the basis for 2D cursor control.

Mentions: Artificial mapping does not follow the natural relationship between cortical activity and arm/hand movement. Rather, this method either remaps cortical activity into a different movement of a device, or maps cortical activity to device movement using arbitrary decoding weights (Fetz, 2007; Moritz et al., 2008; Schalk et al., 2008; Ganguly and Carmena, 2009; McFarland et al., 2010; Wang et al., 2013). A BCI user has to learn this novel mapping in order to control an external device with his brain activity. For example, Wang et al. remapped cortical activity associated with thumb and elbow movements to two-dimensional (2D) movements of a computer cursor (Figure 2; Wang et al., 2013). During BCI training, the participant was told to associate four flexion/extension movement patterns with four cursor movement directions in x — y planes. Attempted movements of thumb, elbow, both thumb and elbow, and no thumb or elbow (rest) were associated with for left, right, up and down, respectively. It is also worth noting that this approach allowed the participant to move the cursor in any directions in the 2D workspace and not just up, down, left, and right. The participant, an individual with long-term paralysis due to cervical spinal cord injury, learned this mapping and achieved cortical control of a computer cursor.


Brain computer interface learning for systems based on electrocorticography and intracortical microelectrode arrays.

Hiremath SV, Chen W, Wang W, Foldes S, Yang Y, Tyler-Kabara EC, Collinger JL, Boninger ML - Front Integr Neurosci (2015)

Artificial mapping for ECoG-based brain control used in Wang et al. (2013). Brain activities corresponding to thumb and elbow movements are mapped on to a two-dimensional workspace to serve as the basis for 2D cursor control.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4462099&req=5

Figure 2: Artificial mapping for ECoG-based brain control used in Wang et al. (2013). Brain activities corresponding to thumb and elbow movements are mapped on to a two-dimensional workspace to serve as the basis for 2D cursor control.
Mentions: Artificial mapping does not follow the natural relationship between cortical activity and arm/hand movement. Rather, this method either remaps cortical activity into a different movement of a device, or maps cortical activity to device movement using arbitrary decoding weights (Fetz, 2007; Moritz et al., 2008; Schalk et al., 2008; Ganguly and Carmena, 2009; McFarland et al., 2010; Wang et al., 2013). A BCI user has to learn this novel mapping in order to control an external device with his brain activity. For example, Wang et al. remapped cortical activity associated with thumb and elbow movements to two-dimensional (2D) movements of a computer cursor (Figure 2; Wang et al., 2013). During BCI training, the participant was told to associate four flexion/extension movement patterns with four cursor movement directions in x — y planes. Attempted movements of thumb, elbow, both thumb and elbow, and no thumb or elbow (rest) were associated with for left, right, up and down, respectively. It is also worth noting that this approach allowed the participant to move the cursor in any directions in the 2D workspace and not just up, down, left, and right. The participant, an individual with long-term paralysis due to cervical spinal cord injury, learned this mapping and achieved cortical control of a computer cursor.

Bottom Line: A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively.We call this process BCI learning, and it often requires significant effort and time.Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Medicine and Rehabilitation, University of Pittsburgh Pittsburgh, PA, USA ; Department of Veterans Affairs, Human Engineering Research Laboratories Pittsburgh, PA, USA.

ABSTRACT
A brain-computer interface (BCI) system transforms neural activity into control signals for external devices in real time. A BCI user needs to learn to generate specific cortical activity patterns to control external devices effectively. We call this process BCI learning, and it often requires significant effort and time. Therefore, it is important to study this process and develop novel and efficient approaches to accelerate BCI learning. This article reviews major approaches that have been used for BCI learning, including computer-assisted learning, co-adaptive learning, operant conditioning, and sensory feedback. We focus on BCIs based on electrocorticography and intracortical microelectrode arrays for restoring motor function. This article also explores the possibility of brain modulation techniques in promoting BCI learning, such as electrical cortical stimulation, transcranial magnetic stimulation, and optogenetics. Furthermore, as proposed by recent BCI studies, we suggest that BCI learning is in many ways analogous to motor and cognitive skill learning, and therefore skill learning should be a useful metaphor to model BCI learning.

No MeSH data available.