**4. Conclusion**

The methodology of EBF studies has often been subject to criticism (Kline et al., 2002; Loo & Barkley, 2005; Pelham & Waschbusch, 2006; Ramirez et al., 2001; Rickles et al., 1982). While some concerns are undoubtedly warranted, much effort has been put in establishing strict guidelines for EBF therapy and this has been met with positive results (Arns et al., 2009; La Vaque et al., 2002). Double-blind, randomised and placebo controlled experiments are unfortunately not always an option. Blinding requires a control condition that is indistinguishable from the treatment condition, which is often technically not feasible. Randomisation, while powerful, is only useful when the target sample is either well-known or homogenous to avoid samples being treated with inadequate protocols (Hauri, 1981; Hauri et al., 1982). Finally, a placebo condition, especially in the case of ADHD, is problematic from an ethical viewpoint, as denying patients a standard and efficacious

(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

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

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

The methodology of EBF studies has often been subject to criticism (Kline et al., 2002; Loo & Barkley, 2005; Pelham & Waschbusch, 2006; Ramirez et al., 2001; Rickles et al., 1982). While some concerns are undoubtedly warranted, much effort has been put in establishing strict guidelines for EBF therapy and this has been met with positive results (Arns et al., 2009; La Vaque et al., 2002). Double-blind, randomised and placebo controlled experiments are unfortunately not always an option. Blinding requires a control condition that is indistinguishable from the treatment condition, which is often technically not feasible. Randomisation, while powerful, is only useful when the target sample is either well-known or homogenous to avoid samples being treated with inadequate protocols (Hauri, 1981; Hauri et al., 1982). Finally, a placebo condition, especially in the case of ADHD, is problematic from an ethical viewpoint, as denying patients a standard and efficacious

maintained (Morgenthaler et al., 2006; Morin et al., 2006; Morin et al., 1999).

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

(Hauri, 1981; et al., 1982).

knowledge with advanced technology.

**4. Conclusion** 

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

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 compilation of strategies feasible.

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.
