**3. EBF as treatment of insomnia**

Insomnia is a most pervasive disorder, affecting about 15% of the general population while 6% meet clinical (DSM-IV) criteria (Ohayon, 2002) and interferes with cognition, quality of life, job performance and represents a multi-billion dollar burden on healthcare providers (Daley et al., 2009; Ebben & Spielman, 2009; Edinger et al., 2004). Insomnia can be subdivided into primary and co-morbid insomnia with the most salient symptoms being difficulty initiating and/or maintaining sleep (Espie, 2007). Causes of primary insomnia include physiological, cognitive and behavioural factors (Espie, 2007). Symptoms and duration are related to severity and persistence of stressors (Morin et al., 2006).

To better understand the possible therapeutic targets of insomnia, the so-called "3P model" has been proposed (Ebben & Spielman, 2009). This model specifies three categories of factors influencing the risk at developing or worsening insomnia: predisposing, precipitating and perpetuating factors. The first category constitutes genetic factors or personality traits, such as increased basal level of anxiety or hyperarousal (Drake et al., 2004), whereas precipitating events represent work and educational stress together with health and emotional problems (Bastien et al., 2004). Finally, perpetuating factors, such as continuous stress and poor sleep hygiene, may cause the actual transition to chronic insomnia and complete the vicious circle.

Pharmacological treatment of insomnia with sedative-hypnotic agents has seen a steady decline over the past (Aldrich, 1992; Walsh & Schweitzer, 1999), because of side effects, discontinuation discomfort, and the risk of developing drug tolerance or dependency (Ebben & Spielman, 2009; Walsh & Schweitzer, 1999). Alternative treatment options that have been met with success are cognitive-behavioural therapy (CBT) (Ebben & Spielman, 2009; Espie, 1999; Morin et al., 1999; Morin et al., 1994; Murtagh & Greenwood, 1995; Siebern & Manber, 2010) or treatments

EEG-Biofeedback as a Tool to Modulate Arousal:

2010; Hoedlmoser et al., 2008).

**3.2 Target brain activity**

Trends and Perspectives for Treatment of ADHD and Insomnia 439

individual strategies (Berner et al., 2006; Cortoos et al., 2009; Hauri et al., 1982; Hoedlmoser et al., 2008). Training sessions (Table 3) are usually blocked (e.g., 3 minute intervals) during which a threshold of activity expressed as a percentage of, or within a predefined band around the baseline, must be maintained for 250–500 ms (Berner et al., 2006; Cortoos et al.,

Insomniacs differ from good sleepers in terms of their EEG profile (Fig. 3), especially exhibiting large spectral decreases in the lower frequency bands (delta, theta) (Merica et al., 1998) and attenuated sigma activity, corresponding to less occurrences of sleep spindles (Besset et al., 1998). These findings have led to the design of EBF therapies aimed at either increasing theta activity, due to its close relationship with drowsiness and early sleep stages, or SMR activity, as this rhythm overlaps with the sigma range and is believed to stimulate sleep spindle occurrence which in turn is key to further progression into deeper sleep stages

Fig. 3. Insomniacs and normal sleepers have different EEG during stage 1 sleep. Insomniacs (solid line) have reduced delta, theta and alpha activity, but higher levels of beta activity compared to normal sleepers (dashed line) during early stages of sleep. Y-axis: average power over all participants in specific frequency band. X-axis: normalized duration of sleep

stage 1, each of the 50 dots marks a 2% interval. From: Merica et al., 1998.

increasing body temperature (e.g., physical exercise, hot bath before bed), which has recently been shown to hasten sleep onset (Van Someren, 2006). Whereas CBT causes sustained improvements and reduces sleep complaints, one fifth of the patients does not respond to the intervention (Cortoos et al., 2010; Harvey & Payne, 2002; Morin, 2006). EBF therapy for insomnia could be a safer alternative to medication and may offer treatment where CBT fails.

The EEG profile of insomniacs (Fig. 3) consists of increased levels of beta activity especially during the sleep-onset period and early sleep stages (Merica et al., 1998). These observations may be interpreted as evidence of cognitive hyperarousal, which is in line with the often reported 'racing thoughts' of insomniacs (Bastien et al., 2003; Buysse et al., 2008; Buysse et al., 2008; Freedman, 1986; Harvey & Payne, 2002; Jacobs et al., 1993; Lamarche & Ogilvie, 1997; Merica, et al., 1998; Merica & Gaillard, 1992; Nofzinger et al., 1999; Perlis et al., 2001). In addition, elevated levels of alpha activity at sleep onset (Besset et al., 1998; Krystal et al., 2002) as well as a decrease in delta activity during non-REM sleep (Merica et al., 1998; Merica & Gaillard, 1992) have been reported. Furthermore, it has been demonstrated that insomniacs produce less spontaneous waking SMR activity than controls (P. Hauri, 1981; Krystal et al., 2002). One interesting aspect about the SMR is that it lies in the same frequency range as sleep spindles (Sterman, et al., 1970). Spindles are the hallmark waveform of stage 2 sleep, and their occurrence is reduced in insomniacs (Besset et al., 1998), possibly resulting in lighter and more fragmented sleep (Glenn & Steriade, 1982; Perlis et al., 2001).

#### **3.1 Training duration and feedback**

Protocols for EEG-biofeedback in insomnia are quite similar in many respects to the ones used in the treatment of ADHD, e.g. patients usually receive feedback and reward in the form of auditory and/or visual stimuli and are encouraged to search for their own


 **P/R/B**= Placebo/Randomized/Blind (- = no, + = yes). **N(m)**: Number of participants (males), **Freq.**: Target frequency (increase/decrease), **?** = data unavailable

Table 3. Overview of EBG group studies aimed at improving sleep.

individual strategies (Berner et al., 2006; Cortoos et al., 2009; Hauri et al., 1982; Hoedlmoser et al., 2008). Training sessions (Table 3) are usually blocked (e.g., 3 minute intervals) during which a threshold of activity expressed as a percentage of, or within a predefined band around the baseline, must be maintained for 250–500 ms (Berner et al., 2006; Cortoos et al., 2010; Hoedlmoser et al., 2008).

#### **3.2 Target brain activity**

438 Neuroimaging – Cognitive and Clinical Neuroscience

increasing body temperature (e.g., physical exercise, hot bath before bed), which has recently been shown to hasten sleep onset (Van Someren, 2006). Whereas CBT causes sustained improvements and reduces sleep complaints, one fifth of the patients does not respond to the intervention (Cortoos et al., 2010; Harvey & Payne, 2002; Morin, 2006). EBF therapy for insomnia could be a safer alternative to medication and may offer treatment where CBT fails. The EEG profile of insomniacs (Fig. 3) consists of increased levels of beta activity especially during the sleep-onset period and early sleep stages (Merica et al., 1998). These observations may be interpreted as evidence of cognitive hyperarousal, which is in line with the often reported 'racing thoughts' of insomniacs (Bastien et al., 2003; Buysse et al., 2008; Buysse et al., 2008; Freedman, 1986; Harvey & Payne, 2002; Jacobs et al., 1993; Lamarche & Ogilvie, 1997; Merica, et al., 1998; Merica & Gaillard, 1992; Nofzinger et al., 1999; Perlis et al., 2001). In addition, elevated levels of alpha activity at sleep onset (Besset et al., 1998; Krystal et al., 2002) as well as a decrease in delta activity during non-REM sleep (Merica et al., 1998; Merica & Gaillard, 1992) have been reported. Furthermore, it has been demonstrated that insomniacs produce less spontaneous waking SMR activity than controls (P. Hauri, 1981; Krystal et al., 2002). One interesting aspect about the SMR is that it lies in the same frequency range as sleep spindles (Sterman, et al., 1970). Spindles are the hallmark waveform of stage 2 sleep, and their occurrence is reduced in insomniacs (Besset et al., 1998), possibly resulting in lighter and more

Protocols for EEG-biofeedback in insomnia are quite similar in many respects to the ones used in the treatment of ADHD, e.g. patients usually receive feedback and reward in the form of auditory and/or visual stimuli and are encouraged to search for their own

**N (m) Age Electrodes**

Total: 

 

Total: 

 

**P/R/B**= Placebo/Randomized/Blind (- = no, + = yes). **N(m)**: Number of participants (males), **Freq.**:

**/Ref** 

C3 /A2

/ FCz

FPz & Cz /A2

C3 /A2 **Freq. Stim/**

T7&C3/A2

SMR

SMR

SMR 

 Visual & Auditory

SMR Visual &

Auditory

Visual 20/

**Reward** 

Visual 24.8

Visual 25.4/ 60 min 27.8/60 min

**#Ses./ Dur.** 

(15-62) / 60 min

1 / 4x10 min

10/ 24 min

20 min

fragmented sleep (Glenn & Steriade, 1982; Perlis et al., 2001).

**P/R/B**



EBF/Sham +/+/+ Cz

+/+/+


Table 3. Overview of EBG group studies aimed at improving sleep.

Target frequency (increase/decrease), **?** = data unavailable

 

 Total: 

 

**3.1 Training duration and feedback** 

**Study Conds. Control** 

[1] EBF() [2] EBF(SMR)

[1] EBF [2] Sham

[1] EBF [2] EMG [3] Control

Hauri,1981 [1] EBF+EMG [2] EBF [3] EMG [4] Control

Hauri et al.,1982

Berner et al., 2006

Hoedlmoser et al., 2008

Cortoos et al., 2009

Insomniacs differ from good sleepers in terms of their EEG profile (Fig. 3), especially exhibiting large spectral decreases in the lower frequency bands (delta, theta) (Merica et al., 1998) and attenuated sigma activity, corresponding to less occurrences of sleep spindles (Besset et al., 1998). These findings have led to the design of EBF therapies aimed at either increasing theta activity, due to its close relationship with drowsiness and early sleep stages, or SMR activity, as this rhythm overlaps with the sigma range and is believed to stimulate sleep spindle occurrence which in turn is key to further progression into deeper sleep stages

Fig. 3. Insomniacs and normal sleepers have different EEG during stage 1 sleep. Insomniacs (solid line) have reduced delta, theta and alpha activity, but higher levels of beta activity compared to normal sleepers (dashed line) during early stages of sleep. Y-axis: average power over all participants in specific frequency band. X-axis: normalized duration of sleep stage 1, each of the 50 dots marks a 2% interval. From: Merica et al., 1998.

EEG-Biofeedback as a Tool to Modulate Arousal:

compilation of strategies feasible.

*322*(5903), 876-880.

**5. References** 

Trends and Perspectives for Treatment of ADHD and Insomnia 441

treatment (i.e., medication) is in conflict with the Declaration of Helsinki (Vernon et al., 2004). Employing sham (random frequency) feedback (Hoedlmoser et al., 2008; Logemann et al., 2010) is therefore not always an option when treating patients. Thus, apart from reaching certain endpoints of treatment, the further validation of EBF therapy is likely to depend on the observation of complimentary physiological changes, e.g., obtained from neuroimaging

Motivation and cognitive strategies are also important aspects to consider (Bregman & McAllister, 1982; Meichenbaum, 1976). If participants are motivated and rewarded for their success they will put effort into the therapy, whereas lack thereof leads to frustration and possibly resignation (Huang et al., 2006). Good methodology can compensate for possible expectancy effects, i.e., improved symptoms like decreases in sleep onset latency induced by the sheer hope of becoming better through therapy (Hauri et al., 1982). However, providing sham feedback, which lacks obvious rewards, bears the risk of the participant becoming unmotivated, ceasing effort and thus confounding the comparison between control and experimental condition (Logemann et al., 2010). In addition, the instructions given to participants in the EBF studies reviewed here do not go beyond the direction to meet some specified criterion, i.e., increasing an onscreen bar towards a target value. The general idea is that participants need to search for their own strategies to modulate their brain activity. In our view, this is unfortunate, because good instructions/guidance can increase participant compliance and speed of learning (Weinert et al., 1989). While individual strategies are likely to vary greatly, an opportunity for future research presents itself in the collection of these strategies and finding patterns that may be useful to guide participants towards success more efficiently. Interestingly, Gevensleben et al. (2009) report on having queried individual strategies of their participants (albeit without further analysis), making future

Technological advances have made it possible to record high-density EEG data from several hundred electrodes at once (Dornhege et al., 2006). However, current EBF studies seldom record from more than two active electrodes (Tables 2 and 3). With ongoing developments towards ever more powerful and cost-effective computational equipment, it is feasible that future research should focus on the opportunities these advances can offer EBF, possibly in combination with tools from the field of BCI (e.g., more sophisticated algorithms, spatial filtering allowing feedback on localized anatomical structures and less artefacts). Despite some (methodological) issues that have subjected the field to scepticism, recent developments give rise to optimism, as stricter guidelines are increasingly being adhered to and new avenues continue to be explored (e.g., SCP feedback and tele-neurofeedback as in Cortoos et al., 2009). Overall, from the studies reviewed here we conclude that EBF is a promising tool for treating

disorders of arousal, which offers many opportunities for future research.

Aldrich, M. S. (1992). Sleep disorders. *Curr Opin Neurol Neurosurg, 5*(2), 240-246.

Alhambra, M. A., Fowler, T. P., & Alhambra, A. A. (1995). EEG biofeedback: A new treatment option for ADD/ADHD. *Journal of Neurotherapy, 1*(2), 39-43. Alkire, M. T., Hudetz, A. G., & Tononi, G. (2008). Consciousness and anesthesia. *Science,* 

Angelakis, E., Stathopoulou, S., Frymiare, J. L., Green, D. L., Lubar, J. F., & Kounios, J.

(2007). EEG neurofeedback: a brief overview and an example of peak alpha

experiments or other biomarker assays (Frank & Hargreaves, 2003).

(Berner et al., 2006; Budzynski, 1973; Hauri, 1981; Sittenfeld, 1972; Steriade, 2003). The application of either protocol depends on the insomnia sub-population: theta feedback (enhancement training) is used for patients with difficulty initiating sleep, whereas SMR/sigma feedback is best used on patients that have problems maintaining sleep. The importance of disentangling insomnia subtypes is further illustrated by the studies of Hauri et al. (1981,1982). Even though all participants showed a trend towards improvement, the experimental groups (i.e. theta feedback, SMR feedback) did not differ, which could be attributed to participants having received treatment unsuitable to the underlying symptoms (Hauri, 1981; et al., 1982).

#### **3.3 Efficacy of EEG-biofeedback in the treatment of insomnia**

A pioneering case study used theta training to treat an insomnia patient and observed a near doubling of theta activity by the end of the 11-week (one session per week) EBF training, together with vastly decreased sleep-onset latency (from 54 to 16 minutes), an increase in total sleep time and a halving in intrusive thoughts (Bell, 1979). Recent studies have compared SMR training with pseudo-EBF training and reported positive results with respect to the total sleep time and the sleep latency (Berner et al., 2006; Hoedlmoser et al., 2008). Cortoos et al. (2009) compared electromyography (EMG) biofeedback, aimed at reducing muscle tension and relaxation, with an EBF protocol of SMR increase and simultaneous theta-, and beta-band suppression. Both groups showed decreases in sleep latency (-8.5 and -12.3 minutes respectively) and time awake after sleep onset. It is noteworthy that participants were trained to apply electrodes and initiate training in their home environment and experimental control was established remotely through the internet, making this "tele-neurofeedback" protocol an interesting example of fusing established knowledge with advanced technology.

In contrast to the case of ADHD where subjective ratings largely define outcome measures (Table 2), efficacy and validity of EBF-therapy for insomniacs is easier to assess through objective measures such as total sleep time, sleep-onset latency and the number of nightly awakenings. In 1998, the American Academy of Sleep Medicine recommended biofeedback in general, including EMG-biofeedback, as treatment for insomnia and classified it as "probably efficacious", based on the Guidelines for Evaluation of Clinical Efficacy of Psychophysiological Interventions (Table 3). In the update of 1999–2004, this rating was maintained (Morgenthaler et al., 2006; Morin et al., 2006; Morin et al., 1999).
