**EEG-Biofeedback as a Tool to Modulate Arousal: Trends and Perspectives for Treatment of ADHD and Insomnia**

B. Alexander Diaz, Lizeth H. Sloot,

Huibert D. Mansvelder and Klaus Linkenkaer-Hansen *Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Neuroscience Campus Amsterdam, VU University Amsterdam, Amsterdam The Netherlands* 

#### **1. Introduction**

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voxel proton magnetic resonance spectroscopy in the healthy elderly: subcorticalfrontal axonal N-acetylaspartate levels are correlated with fluid cognitive abilities independent of structural brain changes. *Neuroimage,* Vol.12, No.6, (2000/12/09), EEG-biofeedback (EBF) is a method to provide information about a person's brain state using real-time processing of electroencephalographic data (Budzynski, 1973; Morin, 2006). The idea behind EBF training is that by giving the participant access to a physiological state she will be able to modulate this state in a desired direction. As such EBF makes use of a brain-computer interface (BCI), in itself a field of study that has seen rapidly growing interest over recent years (Felton et al., 2007; Kübler, Kotchoubey et al., 2001; Leuthardt et al., 2006; Schalk et al., 2007). There is a distinction between using BCI to gain control over an external device or to use it to modify the internal state of the user. The former has seen fascinating applications in facilitating control of prosthetics (Nicolelis, 2003) or in offering new channels of communication to the paralysed (Birbaumer et al., 1999; Krusienski et al., 2006; Krusienski et al., 2008). EEG biofeedback belongs to the latter category as it aims to provide a means for the user to modify her own cognition or behaviour through feedback on specific EEG characteristics (Fig. 1). EBF therapy should, after repeated training, result in improved brain states or an effective internalized strategy to invoke such a brain state.

EEG-biofeedback (EBF) was first used in operant conditioning studies on cats in the 1960s. By rewarding the generation of the sensori-motor rhythm (SMR, Table 1), cats learned to increase SMR by suppression of voluntary movement (Roth et al., 1967; Sterman et al., 1969; Sterman & Wyrwicka, 1967; Wyrwicka & Sterman, 1968). Interestingly, a lasting effect of the biofeedback training became apparent when the same cats were later used in a doseresponse study of an epileptogenic compound in which they showed significantly elevated seizure thresholds (Sterman, 1977; Sterman et al., 1969). These serendipitous findings motivated the use of biofeedback in research on humans with epilepsy (Sterman, 2006). Because the EEG is altered in several other disorders, biofeedback research has expanded to a range of clinical disorders including addiction (Passini et al., 1977; Peniston & Kulkosky, 1989; Saxby & Peniston, 1995), anxiety (Angelakis et al., 2007), attention-

EEG-Biofeedback as a Tool to Modulate Arousal:

**Band Frequency range (Hz) Hallmark** 

0.1–4 Sleep (stages N3-N4)

 12–14 Spindle range (N2) SMR 12–15 Sensorimotor rhythm

13–30 Cognitive effort, alertness

 4–8 Drowsiness, Sleep (stages N1-N2) 8–13 Relaxed wakefulness, cortical idling

Table 1. All EEG bands from delta to beta have proven relevant for EBF in ADHD and

ADHD has been described as a disorder of decreased CNS arousal and cortical inhibition, partially explaining the symptom normalizing effect psychostimulants have in the treatment of ADHD (Satterfield et al., 1974). These arousal deficits become manifest in lowered skin conductance levels (Barry et al., 2009; Raine et al., 1990; Satterfield et al., 1974), EEG deviations (e.g. increased theta but less beta activity) (Barry et al., 2003a; Barry et al., 2003b; Clarke et al., 2002; Clarke et al., 2001) and are related to CNS dopamine systems and

Insomniacs in contrast, exhibit elevated (cognitive) arousal effectively delaying the transition from wakefulness to sleep or resulting in frequent awakenings, oftentimes directly related to persistent (psychological) stressors (Bonnet, 2010; Bonnet & Arand, 1997; Bonnet & Arand, 2005; Cortoos et al., 2006; Drake et al., 2004; Drummond et al., 2004; Jansson & Linton, 2007; Nofzinger, 2004; Perlis, 2001). Brain areas involved in sleep regulation, arousal and attention are closely related (Brown et al., 2001) possibly explaining the observation that 50% of ADHD children also have difficulties falling asleep and 20% report recurring severe sleep problems (Ball et al., 1997; Stein, 1999). The association between arousal and sleep has classically been described using the EEG, where elevated arousal is associated with beta and gamma (>30 Hz) activity, whereas decreases in arousal are associated with enhanced delta and theta band activity (Alkire et al., 2008; Rechtschaffen

Here we propose that for EBF to have a therapeutic effect it is required that (1) EEG can index (disease-)relevant states of the brain, (2) one can learn to modulate these brain states, (3) training the modulation of brain states causes (lasting and desired) changes to the brain, and (4) EBF-related changes to the brain have cognitive and/or behavioral correlates. In the following, ADHD and insomnia are treated as case examples of disorders that have been proposed to benefit from EEG-biofeedback therapy. We present the evidence that EBF has a therapeutic effect on these disorders and outline trends and perspectives by reviewing

disorder (ADHD) and insomnia.

associated genes (Li et al., 2006).

& Kales, 1968; Steriade et al., 1993).

recent progress in the design of EBF for pre-clinical research.

insomnia.

Trends and Perspectives for Treatment of ADHD and Insomnia 433

In this chapter, we focus on two disorders that share a characteristic arousal component, which EEG-biofeedback therapy attempts to modulate: attention-deficit hyperactivity

deficit/hyperactivity disorder, autism (Coben & Padolsky, 2007; Pineda et al., 2008), depression (Baehr et al., 1997; Hammond, 2005), post-traumatic stress disorder (Peniston & Kulkosky, 1991), and sleep disorders (Cortoos et al., 2009). More recently, research has explored the potential of biofeedback to enhance normal cognition, e.g. to improve attention (Egner et al., 2002; Gruzelier et al., 2006), working memory (Hoedlmoser et al., 2008; Vernon et al., 2003), or athletic performance (Egner & Gruzelier, 2003; Vernon, 2005).

Fig. 1. The concept of EEG-biofeedback. The EEG is recorded [1], a suitable EEG-biomarker is extracted [2] and made available to the participant and correct changes in brain activity are rewarded by, e.g., a visual stimulus indicating success [3]. With repetition, this enables the participant to learn what strategies to employ in order to change brain activity in the desired direction [4].

In spite of the many studies using EBF to improve a clinical condition, the concept awaits a solid theoretical framework and the efficacy of EBF therapy requires further validation to gain widespread acceptance. Nevertheless, EBF holds the prospects to become an alternative to pharmaceutical intervention, where side-effects and dependency are prominent risks. An efficient EBF protocol that enables learning with a moderate number of sessions, will not only be more cost-effective but may bear additional psychological benefits such as avoiding certain stigmata (requiring psychiatric consultation or medication) and giving the participant more control over his/her own treatment. It is also conceivable that the mechanism with which EBF training exerts its therapeutic action is distinct from drug treatment as has been observed, e.g., when comparing neurobiological changes following successful treatment of depression using either cognitive behavioural therapy (CBT) or medication (Kumari, 2006).This would raise the perspective that EBF could be of help to those patients that do not respond to medication.

deficit/hyperactivity disorder, autism (Coben & Padolsky, 2007; Pineda et al., 2008), depression (Baehr et al., 1997; Hammond, 2005), post-traumatic stress disorder (Peniston & Kulkosky, 1991), and sleep disorders (Cortoos et al., 2009). More recently, research has explored the potential of biofeedback to enhance normal cognition, e.g. to improve attention (Egner et al., 2002; Gruzelier et al., 2006), working memory (Hoedlmoser et al., 2008; Vernon

Fig. 1. The concept of EEG-biofeedback. The EEG is recorded [1], a suitable EEG-biomarker is extracted [2] and made available to the participant and correct changes in brain activity are rewarded by, e.g., a visual stimulus indicating success [3]. With repetition, this enables the participant to learn what strategies to employ in order to change brain activity in the

In spite of the many studies using EBF to improve a clinical condition, the concept awaits a solid theoretical framework and the efficacy of EBF therapy requires further validation to gain widespread acceptance. Nevertheless, EBF holds the prospects to become an alternative to pharmaceutical intervention, where side-effects and dependency are prominent risks. An efficient EBF protocol that enables learning with a moderate number of sessions, will not only be more cost-effective but may bear additional psychological benefits such as avoiding certain stigmata (requiring psychiatric consultation or medication) and giving the participant more control over his/her own treatment. It is also conceivable that the mechanism with which EBF training exerts its therapeutic action is distinct from drug treatment as has been observed, e.g., when comparing neurobiological changes following successful treatment of depression using either cognitive behavioural therapy (CBT) or medication (Kumari, 2006).This would raise the perspective that EBF could be of help to

desired direction [4].

those patients that do not respond to medication.

et al., 2003), or athletic performance (Egner & Gruzelier, 2003; Vernon, 2005).

In this chapter, we focus on two disorders that share a characteristic arousal component, which EEG-biofeedback therapy attempts to modulate: attention-deficit hyperactivity disorder (ADHD) and insomnia.


Table 1. All EEG bands from delta to beta have proven relevant for EBF in ADHD and insomnia.

ADHD has been described as a disorder of decreased CNS arousal and cortical inhibition, partially explaining the symptom normalizing effect psychostimulants have in the treatment of ADHD (Satterfield et al., 1974). These arousal deficits become manifest in lowered skin conductance levels (Barry et al., 2009; Raine et al., 1990; Satterfield et al., 1974), EEG deviations (e.g. increased theta but less beta activity) (Barry et al., 2003a; Barry et al., 2003b; Clarke et al., 2002; Clarke et al., 2001) and are related to CNS dopamine systems and associated genes (Li et al., 2006).

Insomniacs in contrast, exhibit elevated (cognitive) arousal effectively delaying the transition from wakefulness to sleep or resulting in frequent awakenings, oftentimes directly related to persistent (psychological) stressors (Bonnet, 2010; Bonnet & Arand, 1997; Bonnet & Arand, 2005; Cortoos et al., 2006; Drake et al., 2004; Drummond et al., 2004; Jansson & Linton, 2007; Nofzinger, 2004; Perlis, 2001). Brain areas involved in sleep regulation, arousal and attention are closely related (Brown et al., 2001) possibly explaining the observation that 50% of ADHD children also have difficulties falling asleep and 20% report recurring severe sleep problems (Ball et al., 1997; Stein, 1999). The association between arousal and sleep has classically been described using the EEG, where elevated arousal is associated with beta and gamma (>30 Hz) activity, whereas decreases in arousal are associated with enhanced delta and theta band activity (Alkire et al., 2008; Rechtschaffen & Kales, 1968; Steriade et al., 1993).

Here we propose that for EBF to have a therapeutic effect it is required that (1) EEG can index (disease-)relevant states of the brain, (2) one can learn to modulate these brain states, (3) training the modulation of brain states causes (lasting and desired) changes to the brain, and (4) EBF-related changes to the brain have cognitive and/or behavioral correlates. In the following, ADHD and insomnia are treated as case examples of disorders that have been proposed to benefit from EEG-biofeedback therapy. We present the evidence that EBF has a therapeutic effect on these disorders and outline trends and perspectives by reviewing recent progress in the design of EBF for pre-clinical research.

EEG-Biofeedback as a Tool to Modulate Arousal:

Thompson, 1998).

Barry et al., 2003.).

Siniatchkin et al., 2000; Strehl et al., 2006).

Trends and Perspectives for Treatment of ADHD and Insomnia 435

The increased theta/beta ratio has been proposed as a characteristic biomarker for CNS underarousal (Mann et al., 1992), whereas the SMR has been classically described as reflecting motor inhibition (Sterman & Friar, 1972; Sterman et al., 1970). The vast majority of EBF studies has been inspired by a two-phase protocol of Lubar et al. (1984), in which participants where first trained to increase their SMR and later to inhibit theta activity while simultaneously increasing beta activity (Beauregard & Levesque, 2006; Carmody et al., 2000; Fuchs et al., 2003; Gevensleben et al., 2009; Heywood & Beale, 2003; Holtmann et al., 2009; Kaiser, 1997; Kaiser & Othmer, 2000; Kropotov et al., 2005; La Vaque et al., 2002; Leins et al., 2007; Levesque et al., 2006; Linden et al., 1996; J.F. Lubar et al., 1995; Monastra et al., 2002; Rossiter, 2004; Rossiter, 1998; Rossiter & La Vaque, 1995; Strehl et al., 2006; Thompson &

Fig. 2. Brain activity profiles in children with ADHD differ from healthy controls. Theta/beta-band activity ratio is strongly elevated in ADHD, but differs in spatial

localization between combined (AD/HDcom) and inattentive (AD/HDin) subtypes. (From:

In recent years, however, an interesting new target for EBF has been found in the form of slow cortical potentials (SCPs). These slow event-related DC shifts represent excitation thresholds of large neuronal assemblies and training ADHD patients to increase SCPs robustly improves symptoms of ADHD (Doehnert et al., 2008; Drechsler et al., 2007; Gevensleben et al., 2009; Heinrich et al., 2007; Kropotov et al., 2005; Leins et al., 2007;
