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Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience.

Jensen O, Bahramisharif A, Oostenveld R, Klanke S, Hadjipapas A, Okazaki YO, van Gerven MA - Front Psychol (2011)

Bottom Line: This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior.In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development.These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

View Article: PubMed Central - PubMed

Affiliation: Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Netherlands.

ABSTRACT
Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain-computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

No MeSH data available.


Related in: MedlinePlus

Alpha activity in right sensorimotor areas predicts performance in a somatosensory working memory task engaging left hemisphere areas. This implies that functional inhibition of the right somatosensory cortex by alpha activity is predictive of successful performance. (A) Patterns of electrical stimuli were presented to the right hand and probed 2 s later. (B) When comparing the 8- to 13-Hz alpha activity in the retention interval between sample and probe (correct–incorrect), the right hemisphere alpha activity was stronger for successful performance. (C) Time–frequency representations of power showed that the task-dependent effect was constrained to the alpha band in the retention interval. (D) The alpha sources predicting successful performance were localized in areas including right sensorimotor and parieto-occipital regions. Reproduced with permission from Haegens et al. (2010).
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Figure 1: Alpha activity in right sensorimotor areas predicts performance in a somatosensory working memory task engaging left hemisphere areas. This implies that functional inhibition of the right somatosensory cortex by alpha activity is predictive of successful performance. (A) Patterns of electrical stimuli were presented to the right hand and probed 2 s later. (B) When comparing the 8- to 13-Hz alpha activity in the retention interval between sample and probe (correct–incorrect), the right hemisphere alpha activity was stronger for successful performance. (C) Time–frequency representations of power showed that the task-dependent effect was constrained to the alpha band in the retention interval. (D) The alpha sources predicting successful performance were localized in areas including right sensorimotor and parieto-occipital regions. Reproduced with permission from Haegens et al. (2010).

Mentions: The human brain produces oscillatory activity in various frequency bands both during rest and while performing cognitive tasks. What is the functional role of oscillatory brain activity? We will first focus on activity in the alpha band (8–13 Hz) since it has a strong signal-to-noise ratio and is often used as a control signal in BCI setups (Pfurtscheller and Neuper, 2006; Lou et al., 2008; Van Gerven et al., 2009; Boord et al., 2010). The alpha rhythm was first reported by Hans Berger in the late 1920s (Berger, 1929). When subjects are resting, it is by far the strongest electrophysiological signal that can be recorded from the human scalp. MEG research using source modeling has localized the sources of the alpha activity to parieto-occipital regions and primary sensorimotor regions around the central sulcus (Hari and Salmelin, 1997; Jensen and Vanni, 2002). Given that alpha activity is strongest when people rest and close their eyes, it has long been thought that it is related to brain states where few mental operations are occurring and thus labeled the “idling rhythm” (Pfurtscheller et al., 1996). Over the last decade, the idling hypothesis has been challenged (Klimesch et al., 2006; Palva and Palva, 2007; Thut and Miniussi, 2009; Jensen and Mazaheri, 2010). In particular it has been demonstrated that alpha activity actually can increase with cognitive load (Jensen et al., 2002; Scheeringa et al., 2009; Haegens et al., 2010; Khader et al., 2010; Meeuwissen et al., 2010). For instance, during working memory retention when no visual input are introduced, the alpha activity increases with the number of items that the subject has to retain (Jensen et al., 2002; Jensen, 2006; Tuladhar et al., 2007; Scheeringa et al., 2009). Other studies have used cognitive tasks which directly manipulate the engagement and disengagement of certain regions (Jokisch and Jensen, 2007; Rihs et al., 2007; Van Der Werf et al., 2008; Sauseng et al., 2009; Haegens et al., 2010; Romei et al., 2010; Freunberger et al., 2011). For instance, it is well established that alpha activity is depressed when covert attention is directed toward visual stimuli. However, when attention is directed toward auditory stimuli, the alpha activity over occipital areas increases (Fu et al., 2001). Other studies in which attention was directed toward the left or right visual hemifield have established that alpha activity is depressed in the hemisphere contralateral to the attended hemifield, while it often increases ipsilaterally (Worden et al., 2000; Thut et al., 2006; Rihs et al., 2007; Van Gerven et al., 2009; Cosmelli et al., 2011; Gould et al., 2011). These findings support the notion that the alpha activity reflects inhibition of regions not involved in a given task (Klimesch et al., 2006). This inhibition will serve to allocate resources to areas involved in the actual processing. Indeed it has been suggested that oscillatory activity subserves a gating function (Lopes Da Silva, 1991). In the light of recent empirical findings, this view has been pushed further in the “gating by inhibition hypothesis” (Jensen and Mazaheri, 2010). According to this hypothesis, task-irrelevant areas are inhibited by the alpha activity in order to actively gate information to the task-relevant areas. By this principle the alpha activity can shape the functional architecture of the working brain. This idea is supported by several recent findings which have demonstrated that if the alpha activity is not strong enough in the task-irrelevant areas, then performance is suboptimal. For instance, in a recent MEG study, oscillatory brain activity was investigated in a somatosensory working memory task (Haegens et al., 2010). Stimuli were delivered to the right hand and thus engaged the left hemisphere. Interestingly, behavioral performance correlated with the magnitude of widespread right hemispheric alpha activity from the retention interval (between sample and probe) including right somatosensory areas (Figure 1). Support for the notion that the alpha activity serves to causally inhibit was found using paradigms engaging respectively the left or the right hemisphere together with TMS and EEG combined. Entraining the task-irrelevant hemisphere by TMS pulses at alpha frequency serves to increase performance (Sauseng et al., 2009; Romei et al., 2010).


Using brain-computer interfaces and brain-state dependent stimulation as tools in cognitive neuroscience.

Jensen O, Bahramisharif A, Oostenveld R, Klanke S, Hadjipapas A, Okazaki YO, van Gerven MA - Front Psychol (2011)

Alpha activity in right sensorimotor areas predicts performance in a somatosensory working memory task engaging left hemisphere areas. This implies that functional inhibition of the right somatosensory cortex by alpha activity is predictive of successful performance. (A) Patterns of electrical stimuli were presented to the right hand and probed 2 s later. (B) When comparing the 8- to 13-Hz alpha activity in the retention interval between sample and probe (correct–incorrect), the right hemisphere alpha activity was stronger for successful performance. (C) Time–frequency representations of power showed that the task-dependent effect was constrained to the alpha band in the retention interval. (D) The alpha sources predicting successful performance were localized in areas including right sensorimotor and parieto-occipital regions. Reproduced with permission from Haegens et al. (2010).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Figure 1: Alpha activity in right sensorimotor areas predicts performance in a somatosensory working memory task engaging left hemisphere areas. This implies that functional inhibition of the right somatosensory cortex by alpha activity is predictive of successful performance. (A) Patterns of electrical stimuli were presented to the right hand and probed 2 s later. (B) When comparing the 8- to 13-Hz alpha activity in the retention interval between sample and probe (correct–incorrect), the right hemisphere alpha activity was stronger for successful performance. (C) Time–frequency representations of power showed that the task-dependent effect was constrained to the alpha band in the retention interval. (D) The alpha sources predicting successful performance were localized in areas including right sensorimotor and parieto-occipital regions. Reproduced with permission from Haegens et al. (2010).
Mentions: The human brain produces oscillatory activity in various frequency bands both during rest and while performing cognitive tasks. What is the functional role of oscillatory brain activity? We will first focus on activity in the alpha band (8–13 Hz) since it has a strong signal-to-noise ratio and is often used as a control signal in BCI setups (Pfurtscheller and Neuper, 2006; Lou et al., 2008; Van Gerven et al., 2009; Boord et al., 2010). The alpha rhythm was first reported by Hans Berger in the late 1920s (Berger, 1929). When subjects are resting, it is by far the strongest electrophysiological signal that can be recorded from the human scalp. MEG research using source modeling has localized the sources of the alpha activity to parieto-occipital regions and primary sensorimotor regions around the central sulcus (Hari and Salmelin, 1997; Jensen and Vanni, 2002). Given that alpha activity is strongest when people rest and close their eyes, it has long been thought that it is related to brain states where few mental operations are occurring and thus labeled the “idling rhythm” (Pfurtscheller et al., 1996). Over the last decade, the idling hypothesis has been challenged (Klimesch et al., 2006; Palva and Palva, 2007; Thut and Miniussi, 2009; Jensen and Mazaheri, 2010). In particular it has been demonstrated that alpha activity actually can increase with cognitive load (Jensen et al., 2002; Scheeringa et al., 2009; Haegens et al., 2010; Khader et al., 2010; Meeuwissen et al., 2010). For instance, during working memory retention when no visual input are introduced, the alpha activity increases with the number of items that the subject has to retain (Jensen et al., 2002; Jensen, 2006; Tuladhar et al., 2007; Scheeringa et al., 2009). Other studies have used cognitive tasks which directly manipulate the engagement and disengagement of certain regions (Jokisch and Jensen, 2007; Rihs et al., 2007; Van Der Werf et al., 2008; Sauseng et al., 2009; Haegens et al., 2010; Romei et al., 2010; Freunberger et al., 2011). For instance, it is well established that alpha activity is depressed when covert attention is directed toward visual stimuli. However, when attention is directed toward auditory stimuli, the alpha activity over occipital areas increases (Fu et al., 2001). Other studies in which attention was directed toward the left or right visual hemifield have established that alpha activity is depressed in the hemisphere contralateral to the attended hemifield, while it often increases ipsilaterally (Worden et al., 2000; Thut et al., 2006; Rihs et al., 2007; Van Gerven et al., 2009; Cosmelli et al., 2011; Gould et al., 2011). These findings support the notion that the alpha activity reflects inhibition of regions not involved in a given task (Klimesch et al., 2006). This inhibition will serve to allocate resources to areas involved in the actual processing. Indeed it has been suggested that oscillatory activity subserves a gating function (Lopes Da Silva, 1991). In the light of recent empirical findings, this view has been pushed further in the “gating by inhibition hypothesis” (Jensen and Mazaheri, 2010). According to this hypothesis, task-irrelevant areas are inhibited by the alpha activity in order to actively gate information to the task-relevant areas. By this principle the alpha activity can shape the functional architecture of the working brain. This idea is supported by several recent findings which have demonstrated that if the alpha activity is not strong enough in the task-irrelevant areas, then performance is suboptimal. For instance, in a recent MEG study, oscillatory brain activity was investigated in a somatosensory working memory task (Haegens et al., 2010). Stimuli were delivered to the right hand and thus engaged the left hemisphere. Interestingly, behavioral performance correlated with the magnitude of widespread right hemispheric alpha activity from the retention interval (between sample and probe) including right somatosensory areas (Figure 1). Support for the notion that the alpha activity serves to causally inhibit was found using paradigms engaging respectively the left or the right hemisphere together with TMS and EEG combined. Entraining the task-irrelevant hemisphere by TMS pulses at alpha frequency serves to increase performance (Sauseng et al., 2009; Romei et al., 2010).

Bottom Line: This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior.In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development.These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

View Article: PubMed Central - PubMed

Affiliation: Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Netherlands.

ABSTRACT
Large efforts are currently being made to develop and improve online analysis of brain activity which can be used, e.g., for brain-computer interfacing (BCI). A BCI allows a subject to control a device by willfully changing his/her own brain activity. BCI therefore holds the promise as a tool for aiding the disabled and for augmenting human performance. While technical developments obviously are important, we will here argue that new insight gained from cognitive neuroscience can be used to identify signatures of neural activation which reliably can be modulated by the subject at will. This review will focus mainly on oscillatory activity in the alpha band which is strongly modulated by changes in covert attention. Besides developing BCIs for their traditional purpose, they might also be used as a research tool for cognitive neuroscience. There is currently a strong interest in how brain-state fluctuations impact cognition. These state fluctuations are partly reflected by ongoing oscillatory activity. The functional role of the brain state can be investigated by introducing stimuli in real-time to subjects depending on the actual state of the brain. This principle of brain-state dependent stimulation may also be used as a practical tool for augmenting human behavior. In conclusion, new approaches based on online analysis of ongoing brain activity are currently in rapid development. These approaches are amongst others informed by new insight gained from electroencephalography/magnetoencephalography studies in cognitive neuroscience and hold the promise of providing new ways for investigating the brain at work.

No MeSH data available.


Related in: MedlinePlus